IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
AN IMPROVED TECHNIQUE FOR HUMAN FACE RECOGNITION USING IMAGE PROCESSINGijiert bestjournal
Face recognition is a computer application technique for automatically identifying or
verifying a person from a digital image or a video frame source. To do this is by comparing
selected facial features from the digital image and a face dataset. It is basically used in
security systems and can be compared to other biometrics such as fingerprint recognition or
eye, iris recognition systems. The main limitation of the current face recognition system is
that they only detect straight faces looking at the camera. Separate versions of the system
could be trained for each head orientation, and the results can be combined using arbitration
methods similar to those presented here. In earlier work, the face position must be centerlight
position; any lighting effect will affect the system. Similarly the eyes of person must be
open and without glass.
Face Liveness Detection for Biometric Antispoofing Applications using Color T...rahulmonikasharma
Face recognition is a widely used biometric approach. Face recognition technology has developed rapidly in recent years and it is more direct, user friendly and convenient compared to other methods. But face recognition systems are vulnerable to spoof attacks made by non-real faces. It is an easy way to spoof face recognition systems by facial pictures such as portrait photographs. A secure system needs Liveness detection in order to guard against such spoofing. In this work, face liveness detection approaches are categorized based on the various types techniques used for liveness detection. This categorization helps understanding different spoof attacks scenarios and their relation to the developed solutions. A review of the latest works regarding face liveness detection works is presented. The main aim is to provide a simple path for the future development of novel and more secured face liveness detection approach.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
The document summarizes research on automated face detection and recognition. It discusses common applications of face detection such as webcam tracking and photo tagging. Face recognition can be used for biometrics, mugshot databases, and detecting fake IDs. The document then compares human and computer abilities in face detection/recognition and describes challenges computers face representing multidimensional face data. It provides a brief history of the field and covers common approaches to face detection and recognition including eigenfaces, Fisherfaces, neural networks, Gabor wavelets, and active shape models. The document also discusses challenges of 3D, video, and comparing face recognition systems.
This document provides an introduction and overview of face recognition and detection. It discusses how face recognition involves identifying faces in images and can operate in verification or identification modes. Key steps in face recognition processing are discussed, including detection, alignment, feature extraction, and matching. Analysis of faces in subspaces is also covered, as are technical challenges such as variability in facial appearance and complexity of face manifolds. Neural networks, AdaBoost methods, and dealing with head rotations in detection are also outlined.
This document summarizes a research paper that proposes using face recognition with the eigenface approach for security at automated teller machines (ATMs). It discusses how current ATM systems have security issues and the eigenface approach could help with identification. The document outlines the eigenface algorithm methodology and implementation steps. It provides examples of screenshots from a prototype system. In conclusion, it discusses how the system could improve security at ATMs and help identify criminals, while future work may enhance the face recognition performance.
IRJET- Pose Varying Face Recognition: ReviewIRJET Journal
This document provides a review of techniques for pose varying face recognition. It begins by outlining some of the key challenges of face recognition across different poses, such as self-occlusion, loss of semantic correspondence, and nonlinear deformation of facial textures. It then categorizes and summarizes general face recognition algorithms as well as 2D and 3D techniques that have been developed to handle pose variations. Specifically, it reviews holistic and local approaches, real view-based matching, pose transformation techniques in both image and feature spaces, and 3D reconstruction methods. The document aims to compare existing approaches and identify promising directions for future research in pose invariant face recognition.
AN IMPROVED TECHNIQUE FOR HUMAN FACE RECOGNITION USING IMAGE PROCESSINGijiert bestjournal
Face recognition is a computer application technique for automatically identifying or
verifying a person from a digital image or a video frame source. To do this is by comparing
selected facial features from the digital image and a face dataset. It is basically used in
security systems and can be compared to other biometrics such as fingerprint recognition or
eye, iris recognition systems. The main limitation of the current face recognition system is
that they only detect straight faces looking at the camera. Separate versions of the system
could be trained for each head orientation, and the results can be combined using arbitration
methods similar to those presented here. In earlier work, the face position must be centerlight
position; any lighting effect will affect the system. Similarly the eyes of person must be
open and without glass.
Face Liveness Detection for Biometric Antispoofing Applications using Color T...rahulmonikasharma
Face recognition is a widely used biometric approach. Face recognition technology has developed rapidly in recent years and it is more direct, user friendly and convenient compared to other methods. But face recognition systems are vulnerable to spoof attacks made by non-real faces. It is an easy way to spoof face recognition systems by facial pictures such as portrait photographs. A secure system needs Liveness detection in order to guard against such spoofing. In this work, face liveness detection approaches are categorized based on the various types techniques used for liveness detection. This categorization helps understanding different spoof attacks scenarios and their relation to the developed solutions. A review of the latest works regarding face liveness detection works is presented. The main aim is to provide a simple path for the future development of novel and more secured face liveness detection approach.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
The document summarizes research on automated face detection and recognition. It discusses common applications of face detection such as webcam tracking and photo tagging. Face recognition can be used for biometrics, mugshot databases, and detecting fake IDs. The document then compares human and computer abilities in face detection/recognition and describes challenges computers face representing multidimensional face data. It provides a brief history of the field and covers common approaches to face detection and recognition including eigenfaces, Fisherfaces, neural networks, Gabor wavelets, and active shape models. The document also discusses challenges of 3D, video, and comparing face recognition systems.
This document provides an introduction and overview of face recognition and detection. It discusses how face recognition involves identifying faces in images and can operate in verification or identification modes. Key steps in face recognition processing are discussed, including detection, alignment, feature extraction, and matching. Analysis of faces in subspaces is also covered, as are technical challenges such as variability in facial appearance and complexity of face manifolds. Neural networks, AdaBoost methods, and dealing with head rotations in detection are also outlined.
This document summarizes a research paper that proposes using face recognition with the eigenface approach for security at automated teller machines (ATMs). It discusses how current ATM systems have security issues and the eigenface approach could help with identification. The document outlines the eigenface algorithm methodology and implementation steps. It provides examples of screenshots from a prototype system. In conclusion, it discusses how the system could improve security at ATMs and help identify criminals, while future work may enhance the face recognition performance.
IRJET- Pose Varying Face Recognition: ReviewIRJET Journal
This document provides a review of techniques for pose varying face recognition. It begins by outlining some of the key challenges of face recognition across different poses, such as self-occlusion, loss of semantic correspondence, and nonlinear deformation of facial textures. It then categorizes and summarizes general face recognition algorithms as well as 2D and 3D techniques that have been developed to handle pose variations. Specifically, it reviews holistic and local approaches, real view-based matching, pose transformation techniques in both image and feature spaces, and 3D reconstruction methods. The document aims to compare existing approaches and identify promising directions for future research in pose invariant face recognition.
IRJET- Credit Card Authentication using Facial RecognitionIRJET Journal
This document describes a proposed system for credit card authentication using facial recognition. The system aims to address security issues with credit card fraud during online transactions. Currently, credit cards often rely on PIN codes for authentication, but PIN codes can be stolen or forgotten. The proposed system uses facial recognition technology to authenticate users during online credit card payments. When users register their credit card, their photo would be taken and their facial features extracted and stored in a database. During a payment, the system would compare the user's live photo to the stored facial features to verify their identity before approving the transaction. The document outlines the facial recognition process, including face detection, feature extraction using Local Binary Patterns, and face matching. It also provides sample
Scale Invariant Feature Transform Based Face Recognition from a Single Sample...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Face recognition systems are becoming increasingly important for security applications like surveillance cameras. They use biometric facial features which are easier for non-collaborating individuals compared to other biometrics. The document outlines the steps for a face recognition system as acquiring an image, detecting faces, recognizing faces to identify individuals. It discusses challenges like illumination, occlusion and methods are categorized as knowledge-based or appearance-based. The problem is to design a system for a robotics lab to detect and recognize frontal faces under changing lighting of at least 50 people, excluding sunglasses. The thesis outline covers literature review, proposed system theory, experiments and results, discussion and future work.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Face Recognition System under Varying Lighting ConditionsIOSR Journals
The document discusses a face recognition system that is robust to varying lighting conditions. It proposes combining illumination normalization preprocessing, local texture-based face representations like Local Binary Patterns (LBP) and Local Ternary Patterns (LTP), and distance transform-based matching. The preprocessing aims to eliminate lighting variations while preserving important appearance details. Local patterns make the representations less sensitive to noise. Experiments show the method outperforms other preprocessors across datasets and lighting conditions, providing a 88.1% verification rate at 0.1% false acceptance. The system is implemented in MATLAB.
Depth-Image-based Facial Analysis between Age Groups and Recognition of 3D FacesIDES Editor
Face recognition is still an open problem. Many 2D
face recognition approaches came into light to achieve high
recognition rate. But these approaches are still challenged by
the changes in illuminations, expressions, pose, noise, etc. A
3D face recognition technique is proposed to overcome such
challenges and to enhance robustness to expression variations.
Here, we compare the person at different age groups with
higher recognition rate in comparison to 2D face recognition
techniques. We propose a two stage procedure of 3D face
recognition based on FLD (Fisher Linear Discriminant), SURF
operator and depth-image. First, FLD is used on depth-image
to perform recognition and then the SURF features of 2D
gray images to carry out the refined recognition. Finally, our
proposed work will increase the robustness in expression
variations.
Face recognition: A Comparison of Appearance Based Approachessadique_ghitm
Face recognition approaches can be divided into three main categories: direct correlation, eigenfaces, and fisherfaces. Direct correlation directly compares pixel intensity values between images. Eigenfaces uses principal component analysis to project faces into a face space defined by eigenvectors. Fisherfaces aims to maximize between-class variations while minimizing within-class variations to better account for differences in lighting and expressions. Pre-processing techniques like color normalization, histogram equalization, and edge detection can improve the accuracy of face recognition systems by reducing the effects of lighting variations. Testing various pre-processing techniques on different approaches found that the fisherfaces method combined with SLBC preprocessing achieved the lowest error rate of 17.8%, followed closely by direct correlation with intensity normalization at 18.
The document discusses a facial recognition system based on locality preserving projections (LPP). It begins by explaining that existing facial recognition systems using PCA and LDA aim to preserve global structure but local structure is more important. It then proposes a system using LPP, which aims to preserve local manifold structure by modeling the image space as a nearest-neighbor graph. The system represents faces as "Laplacianfaces" in a low-dimensional subspace that preserves local structure for more accurate identification. It provides theoretical analysis showing how PCA, LDA and LPP can be derived from different graph models.
The document discusses challenges and approaches for facial emotion recognition. It aims to develop a model-based approach for real-time driver emotion recognition on an embedded platform using parallel processing. Model-based approaches can overcome issues like illumination and pose variations. The document reviews several state-of-the-art methods and discusses challenges like occlusion, lighting distortions, and complex backgrounds. It describes exploring both 2D and 3D techniques for facial feature extraction and expression recognition.
Lecture 10 ming yang - face recognition systemsmustafa sarac
This document provides an overview of face recognition technologies. It discusses applications of face recognition such as security, law enforcement, and entertainment. It describes common face recognition tasks like verification, identification, and search. It outlines challenges in face recognition like variations in pose, expression, lighting, etc. It reviews influential face recognition algorithms from the 1960s to today. It also discusses popular face datasets and benchmark tests. State-of-the-art face recognition systems can achieve over 99% accuracy on constrained datasets but performance decreases significantly for unconstrained images. Research continues to improve face recognition for real-world applications.
Comparative Analysis of Face Recognition Methodologies and TechniquesFarwa Ansari
In the field of computer sciences such as
graphics and also analyzing the image and its processing,
face recognition is the most prominent problem due to the
comprehensive variation of faces and the complexity of
noises and image backgrounds. The purpose and working
of this system is that it identifies the face of a person from
the real time video and verifies the person from the images
store in the database. This paper provides a review of the
methodologies and techniques used for face detection and
recognition. Firstly a brief introduction of Facial
Recognition is given then the review of the face
recognition’s working which has been done until now, is
briefly introduced. Then the next sections covered the
approaches, methodologies, techniques and their
comparison. Holistic, Feature based and Hybrid
approaches are basically used for face recognition
methodologies. Eigen Faces, Fisher Faces and LBP
methodologies were introduced for recognition purpose.
Eigen Faces is most frequently used because of its
efficiencies. To observe the efficient techniques of facial
recognition, there are many scenarios to measure its
performance which are based on real time.
Fake Multi Biometric Detection using Image Quality Assessmentijsrd.com
In the recent era where technology plays a prominent role, persons can be identified (for security reasons) based on their behavioral and physiological characteristics (for example fingerprint, face, iris, key-stroke, signature, voice, etc.) through a computer system called the biometric system. In these kinds of systems the security is still a question mark because of various intruders and attacks. This problem can be solved by improving the security using some efficient algorithms available. Hence the fake person can be identified if he/she uses any synthetic sample of an authenticated person and a fake person who is trying to forge can be identified and authenticated.
This document describes a facial expression recognition system created by Mehwish S. Khan for her Masters in Computer Science. The system uses Viola-Jones algorithm for face detection, uniform Gabor features for feature extraction, and a Multi-Layer Feed Forward Neural Network for classification to distinguish seven universal facial expressions (disgust, anger, fear, happiness, sadness, surprise, and normal) from static images in a person-independent manner. The document includes sections on background research, system requirements, design, and implementation.
This document discusses face detection techniques. It begins with an introduction that defines face detection and discusses why it is important and challenging. It then covers topics like image segmentation, face detection approaches, morphological image processing, and skin color-based face detection. The document analyzes literature on face detection methods and provides descriptions of techniques like thresholding, edge detection, region-based segmentation, and template matching. It also includes a case study on specific face detection software applications and concludes by summarizing the discussed techniques.
Facial expression recognition using pca and gabor with jaffe database 11748EditorIJAERD
This document discusses a facial expression recognition system that uses two different feature extraction methods - Principal Component Analysis (PCA) and Gabor filters - with the JAFFE facial expression database. PCA is used to reduce the dimensionality of the feature space, while Gabor filters are used to extract features due to their ability to encode spatial frequency and orientation information. The system that uses Gabor filters and PCA achieved better accuracy than one that used only PCA. The document provides mathematical background on PCA and Gabor filters and describes the steps of the facial expression recognition algorithm.
Password Authentication Framework Based on Encrypted Negative PasswordIJSRED
This document summarizes a research paper that proposes a new method called Deep Dense Face Detector (DDFD) for multi-view face detection and tagging. DDFD uses a single deep convolutional neural network model to detect faces across a wide range of orientations without requiring pose or landmark annotation. All detected faces are then recognized using Local Binary Patterns Histograms and tagged, achieving 85% accuracy. The proposed method has minimal complexity compared to other recent deep learning object detection methods as it does not require additional components like segmentation or bounding box regression.
Abstract: This paper presents a new face parts information analyzer, as a promising model for detecting faces and locating the facial features in images. The main objective is to build fully automated human facial measurements systems from images with complex backgrounds. Detection of facial features such as eye, nose, and mouth is an important step for many subsequent facial image analysis tasks. The main study of face detection is detect the portion of part and mention the circle or rectangular of the every portion of body. In this paper face detection is depend upon the face pattern which is match the face from the pattern reorganization. The study present a novel and simple model approach based on a mixture of techniques and algorithms in a shared pool based on viola jones object detection framework algorithm combined with geometric and symmetric information of the face parts from the image in a smart algorithm.Keywords: Face detection, Video frames, Viola-Jones, Skin detection, Skin color classification, Face reorganization, Pattern reorganization. Skin Color.
Title: Face Detection Using Modified Viola Jones Algorithm
Author: Alpika Gupta, Dr. Rajdev Tiwari
International Journal of Recent Research in Mathematics Computer Science and Information Technology
ISSN 2350-1022
Paper Publications
The goal of this paper is to present a critical survey of existing literatures on human face detection and recognition over the last 4-5 years. An application for automatic face detection and tracking in video streams from surveillance cameras in public or commercial places is discussed in this paper. Prototype is designed to work with web cameras for the face detection and tracking system based on Visual 2010 C# and Open CV. This system can be used for security purpose to record the visitor face as well as to detect and track the face.
Keywords:- Face Detection, Face Recognition, Open CV, Face Tracking, Video Streams.
Facial image classification and searching –a surveyZac Darcy
Recent developments in the area of image mining have shown the way for incredible growth in
extensively large and detailed image databases. The images which are available in these
databases, if checked, can endow with valuable information to the human users. As one of the
most successful applications of image analysis and understanding, fac
e recognition has
recently gained important attention particularly throughout the past many years. Though
tracking and recognizing face objects is a routine task, building such a system is still an active
research. Among several proposed face rec
ognition schemes, shape based approaches are
possibly the most promising ones. This paper provides an overview of various
classification and retrieval methods that were proposed earlier in literature. Also, this paper
provides a margina
l summary for future research and enhancements in face detection
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This document proposes new task prioritization rules for project execution that aim to improve upon the rules advocated by Critical Chain Project Management (CCPM). It presents the currently accepted CCPM rule for prioritizing tasks within a single project. Through computer simulations of over 970,000 possible cases, the proposed new rule that prioritizes based on the integration point of feeding chains is shown to result in significantly shorter project lead times. The document also proposes a new rule for prioritizing tasks across multiple projects to avoid mixing task priorities between projects. Another simulation compares this rule to the CCPM approach and again finds the proposed rule leads to faster project completion.
This document summarizes a study on implementing Total Productive Maintenance (TPM) in a manufacturing organization to improve Overall Equipment Effectiveness (OEE). TPM aims to maximize equipment availability and minimize downtime through innovative maintenance strategies. The study focuses on measuring OEE and reducing equipment downtime at a manufacturing company. It reviews literature on TPM and maintenance management. The methodology implements TPM steps and measures OEE to increase productivity, quality and profits through improved maintenance policies and continuous production process inspections.
IRJET- Credit Card Authentication using Facial RecognitionIRJET Journal
This document describes a proposed system for credit card authentication using facial recognition. The system aims to address security issues with credit card fraud during online transactions. Currently, credit cards often rely on PIN codes for authentication, but PIN codes can be stolen or forgotten. The proposed system uses facial recognition technology to authenticate users during online credit card payments. When users register their credit card, their photo would be taken and their facial features extracted and stored in a database. During a payment, the system would compare the user's live photo to the stored facial features to verify their identity before approving the transaction. The document outlines the facial recognition process, including face detection, feature extraction using Local Binary Patterns, and face matching. It also provides sample
Scale Invariant Feature Transform Based Face Recognition from a Single Sample...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Face recognition systems are becoming increasingly important for security applications like surveillance cameras. They use biometric facial features which are easier for non-collaborating individuals compared to other biometrics. The document outlines the steps for a face recognition system as acquiring an image, detecting faces, recognizing faces to identify individuals. It discusses challenges like illumination, occlusion and methods are categorized as knowledge-based or appearance-based. The problem is to design a system for a robotics lab to detect and recognize frontal faces under changing lighting of at least 50 people, excluding sunglasses. The thesis outline covers literature review, proposed system theory, experiments and results, discussion and future work.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Face Recognition System under Varying Lighting ConditionsIOSR Journals
The document discusses a face recognition system that is robust to varying lighting conditions. It proposes combining illumination normalization preprocessing, local texture-based face representations like Local Binary Patterns (LBP) and Local Ternary Patterns (LTP), and distance transform-based matching. The preprocessing aims to eliminate lighting variations while preserving important appearance details. Local patterns make the representations less sensitive to noise. Experiments show the method outperforms other preprocessors across datasets and lighting conditions, providing a 88.1% verification rate at 0.1% false acceptance. The system is implemented in MATLAB.
Depth-Image-based Facial Analysis between Age Groups and Recognition of 3D FacesIDES Editor
Face recognition is still an open problem. Many 2D
face recognition approaches came into light to achieve high
recognition rate. But these approaches are still challenged by
the changes in illuminations, expressions, pose, noise, etc. A
3D face recognition technique is proposed to overcome such
challenges and to enhance robustness to expression variations.
Here, we compare the person at different age groups with
higher recognition rate in comparison to 2D face recognition
techniques. We propose a two stage procedure of 3D face
recognition based on FLD (Fisher Linear Discriminant), SURF
operator and depth-image. First, FLD is used on depth-image
to perform recognition and then the SURF features of 2D
gray images to carry out the refined recognition. Finally, our
proposed work will increase the robustness in expression
variations.
Face recognition: A Comparison of Appearance Based Approachessadique_ghitm
Face recognition approaches can be divided into three main categories: direct correlation, eigenfaces, and fisherfaces. Direct correlation directly compares pixel intensity values between images. Eigenfaces uses principal component analysis to project faces into a face space defined by eigenvectors. Fisherfaces aims to maximize between-class variations while minimizing within-class variations to better account for differences in lighting and expressions. Pre-processing techniques like color normalization, histogram equalization, and edge detection can improve the accuracy of face recognition systems by reducing the effects of lighting variations. Testing various pre-processing techniques on different approaches found that the fisherfaces method combined with SLBC preprocessing achieved the lowest error rate of 17.8%, followed closely by direct correlation with intensity normalization at 18.
The document discusses a facial recognition system based on locality preserving projections (LPP). It begins by explaining that existing facial recognition systems using PCA and LDA aim to preserve global structure but local structure is more important. It then proposes a system using LPP, which aims to preserve local manifold structure by modeling the image space as a nearest-neighbor graph. The system represents faces as "Laplacianfaces" in a low-dimensional subspace that preserves local structure for more accurate identification. It provides theoretical analysis showing how PCA, LDA and LPP can be derived from different graph models.
The document discusses challenges and approaches for facial emotion recognition. It aims to develop a model-based approach for real-time driver emotion recognition on an embedded platform using parallel processing. Model-based approaches can overcome issues like illumination and pose variations. The document reviews several state-of-the-art methods and discusses challenges like occlusion, lighting distortions, and complex backgrounds. It describes exploring both 2D and 3D techniques for facial feature extraction and expression recognition.
Lecture 10 ming yang - face recognition systemsmustafa sarac
This document provides an overview of face recognition technologies. It discusses applications of face recognition such as security, law enforcement, and entertainment. It describes common face recognition tasks like verification, identification, and search. It outlines challenges in face recognition like variations in pose, expression, lighting, etc. It reviews influential face recognition algorithms from the 1960s to today. It also discusses popular face datasets and benchmark tests. State-of-the-art face recognition systems can achieve over 99% accuracy on constrained datasets but performance decreases significantly for unconstrained images. Research continues to improve face recognition for real-world applications.
Comparative Analysis of Face Recognition Methodologies and TechniquesFarwa Ansari
In the field of computer sciences such as
graphics and also analyzing the image and its processing,
face recognition is the most prominent problem due to the
comprehensive variation of faces and the complexity of
noises and image backgrounds. The purpose and working
of this system is that it identifies the face of a person from
the real time video and verifies the person from the images
store in the database. This paper provides a review of the
methodologies and techniques used for face detection and
recognition. Firstly a brief introduction of Facial
Recognition is given then the review of the face
recognition’s working which has been done until now, is
briefly introduced. Then the next sections covered the
approaches, methodologies, techniques and their
comparison. Holistic, Feature based and Hybrid
approaches are basically used for face recognition
methodologies. Eigen Faces, Fisher Faces and LBP
methodologies were introduced for recognition purpose.
Eigen Faces is most frequently used because of its
efficiencies. To observe the efficient techniques of facial
recognition, there are many scenarios to measure its
performance which are based on real time.
Fake Multi Biometric Detection using Image Quality Assessmentijsrd.com
In the recent era where technology plays a prominent role, persons can be identified (for security reasons) based on their behavioral and physiological characteristics (for example fingerprint, face, iris, key-stroke, signature, voice, etc.) through a computer system called the biometric system. In these kinds of systems the security is still a question mark because of various intruders and attacks. This problem can be solved by improving the security using some efficient algorithms available. Hence the fake person can be identified if he/she uses any synthetic sample of an authenticated person and a fake person who is trying to forge can be identified and authenticated.
This document describes a facial expression recognition system created by Mehwish S. Khan for her Masters in Computer Science. The system uses Viola-Jones algorithm for face detection, uniform Gabor features for feature extraction, and a Multi-Layer Feed Forward Neural Network for classification to distinguish seven universal facial expressions (disgust, anger, fear, happiness, sadness, surprise, and normal) from static images in a person-independent manner. The document includes sections on background research, system requirements, design, and implementation.
This document discusses face detection techniques. It begins with an introduction that defines face detection and discusses why it is important and challenging. It then covers topics like image segmentation, face detection approaches, morphological image processing, and skin color-based face detection. The document analyzes literature on face detection methods and provides descriptions of techniques like thresholding, edge detection, region-based segmentation, and template matching. It also includes a case study on specific face detection software applications and concludes by summarizing the discussed techniques.
Facial expression recognition using pca and gabor with jaffe database 11748EditorIJAERD
This document discusses a facial expression recognition system that uses two different feature extraction methods - Principal Component Analysis (PCA) and Gabor filters - with the JAFFE facial expression database. PCA is used to reduce the dimensionality of the feature space, while Gabor filters are used to extract features due to their ability to encode spatial frequency and orientation information. The system that uses Gabor filters and PCA achieved better accuracy than one that used only PCA. The document provides mathematical background on PCA and Gabor filters and describes the steps of the facial expression recognition algorithm.
Password Authentication Framework Based on Encrypted Negative PasswordIJSRED
This document summarizes a research paper that proposes a new method called Deep Dense Face Detector (DDFD) for multi-view face detection and tagging. DDFD uses a single deep convolutional neural network model to detect faces across a wide range of orientations without requiring pose or landmark annotation. All detected faces are then recognized using Local Binary Patterns Histograms and tagged, achieving 85% accuracy. The proposed method has minimal complexity compared to other recent deep learning object detection methods as it does not require additional components like segmentation or bounding box regression.
Abstract: This paper presents a new face parts information analyzer, as a promising model for detecting faces and locating the facial features in images. The main objective is to build fully automated human facial measurements systems from images with complex backgrounds. Detection of facial features such as eye, nose, and mouth is an important step for many subsequent facial image analysis tasks. The main study of face detection is detect the portion of part and mention the circle or rectangular of the every portion of body. In this paper face detection is depend upon the face pattern which is match the face from the pattern reorganization. The study present a novel and simple model approach based on a mixture of techniques and algorithms in a shared pool based on viola jones object detection framework algorithm combined with geometric and symmetric information of the face parts from the image in a smart algorithm.Keywords: Face detection, Video frames, Viola-Jones, Skin detection, Skin color classification, Face reorganization, Pattern reorganization. Skin Color.
Title: Face Detection Using Modified Viola Jones Algorithm
Author: Alpika Gupta, Dr. Rajdev Tiwari
International Journal of Recent Research in Mathematics Computer Science and Information Technology
ISSN 2350-1022
Paper Publications
The goal of this paper is to present a critical survey of existing literatures on human face detection and recognition over the last 4-5 years. An application for automatic face detection and tracking in video streams from surveillance cameras in public or commercial places is discussed in this paper. Prototype is designed to work with web cameras for the face detection and tracking system based on Visual 2010 C# and Open CV. This system can be used for security purpose to record the visitor face as well as to detect and track the face.
Keywords:- Face Detection, Face Recognition, Open CV, Face Tracking, Video Streams.
Facial image classification and searching –a surveyZac Darcy
Recent developments in the area of image mining have shown the way for incredible growth in
extensively large and detailed image databases. The images which are available in these
databases, if checked, can endow with valuable information to the human users. As one of the
most successful applications of image analysis and understanding, fac
e recognition has
recently gained important attention particularly throughout the past many years. Though
tracking and recognizing face objects is a routine task, building such a system is still an active
research. Among several proposed face rec
ognition schemes, shape based approaches are
possibly the most promising ones. This paper provides an overview of various
classification and retrieval methods that were proposed earlier in literature. Also, this paper
provides a margina
l summary for future research and enhancements in face detection
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This document proposes new task prioritization rules for project execution that aim to improve upon the rules advocated by Critical Chain Project Management (CCPM). It presents the currently accepted CCPM rule for prioritizing tasks within a single project. Through computer simulations of over 970,000 possible cases, the proposed new rule that prioritizes based on the integration point of feeding chains is shown to result in significantly shorter project lead times. The document also proposes a new rule for prioritizing tasks across multiple projects to avoid mixing task priorities between projects. Another simulation compares this rule to the CCPM approach and again finds the proposed rule leads to faster project completion.
This document summarizes a study on implementing Total Productive Maintenance (TPM) in a manufacturing organization to improve Overall Equipment Effectiveness (OEE). TPM aims to maximize equipment availability and minimize downtime through innovative maintenance strategies. The study focuses on measuring OEE and reducing equipment downtime at a manufacturing company. It reviews literature on TPM and maintenance management. The methodology implements TPM steps and measures OEE to increase productivity, quality and profits through improved maintenance policies and continuous production process inspections.
The document summarizes research evaluating the performance of different TCP congestion control variants (Tahoe, Reno, New Reno) in vehicular ad hoc networks (VANETs) using the routing protocols AODV and DSR. Simulations were conducted using OMNeT++ and SUMO simulators to measure throughput and delay. Results showed that New Reno generally performed better than Reno, while Tahoe performed similarly to New Reno except with larger network sizes where Tahoe had lower delay and higher throughput. New Reno was also found to outperform TCP Reno but not achieve the same performance as TCP Tahoe.
This document discusses built-in self-test (BIST) techniques for testing field programmable gate arrays (FPGAs). It describes how the FPGA can be configured with BIST logic during offline testing to test the programmable logic blocks and interconnects. For online testing, the FPGA can be configured as a processor with an arithmetic logic unit (ALU) that has a BIST feature. The design implements a reduced instruction set computer (RISC) architecture on the FPGA with the ALU and is verified through simulation.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This document discusses the bandwidth requirements of the VARSHA meteorological code when run on parallel computing systems. VARSHA is used for weather forecasting and solves nonlinear partial differential equations to predict future atmospheric states. It was originally developed sequentially but has been parallelized. The document analyzes the parallelization strategy used for VARSHA and assesses its bandwidth utilization on a Ethernet-based cluster computer in order to determine the code's bandwidth needs for efficient parallel execution.
This document discusses using independent component analysis (ICA) to separate electrocardiogram (ECG) signals recorded using high-density montages. It conducted experiments on five subjects using a 98-channel ECG system to record signals. An ICA algorithm was used to separate the P-wave, QRS complex, and T-wave components from the mixed signals. Results showed the components could be clearly separated, confirming ICA is an effective tool for high-density ECG analysis.
The document describes a novel mixed method for order reduction of discrete linear systems. The method uses particle swarm optimization (PSO) to determine the denominator polynomials of the reduced order model. It then uses a polynomial technique to derive the numerator coefficients by equating the original and reduced order transfer functions. This leads to a set of equations that can be solved for the numerator coefficients. The proposed method is illustrated on an 8th order example system from literature. It is found to provide a stable 2nd order reduced model. A lead compensator is then designed and connected to improve the steady state response of the original and reduced order systems.
This document describes an embedded system for measuring the temperature of an automotive engine. It discusses two methods for measuring engine temperature - using a thermistor or using an LM35 temperature sensor.
The system uses an AT89C55 microcontroller as the central processing unit and runs the μC/OS-II real-time operating system. It constructs an automatic measurement system that accurately and reliably measures temperature. Temperature readings are displayed on a graphical LCD and transmitted via UART serial communication.
The document provides details on temperature measurement circuits using a thermistor or LM35 sensor. It also includes diagrams of the schematic for the measurement system embedded in an engine control unit and photos of the prototype temperature measurement system constructed
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
The document analyzes the performance of two routing protocols for mobile ad hoc networks (MANETs) - the Wireless Routing Protocol (WRP) and the Ad Hoc On-Demand Distance Vector (AODV) protocol. It describes the simulation setup used to evaluate the protocols under different scenarios varying offered load, pause time, and node speed. The results show that AODV outperformed WRP in terms of packet delivery ratio, throughput, and average end-to-end delay in most scenarios, demonstrating that AODV is generally better suited for MANETs compared to WRP. The same simulation framework could be used to evaluate other routing protocols.
This document summarizes a study that used computational fluid dynamics (CFD) to analyze different catalytic converter designs with the goal of reducing particulate matter emissions and back pressure. Three catalytic converter models with different wire mesh sizes in each compartment were simulated. The model with a wire mesh size of 1.96mm in the first compartment and 1.61mm in the second (MC-1) had the lowest pressure drop, which would result in lower fuel consumption and higher engine efficiency. In conclusion, optimizing the wire mesh size can both maximize particulate filtration and limit back pressure increase inside the catalytic converter.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
- J.Hemamalini is seeking a goal-oriented role where she can utilize her skills and help the organization achieve its defined goals.
- She has over 4 years of experience in customer service and operations management at Maersk Global Service Centre.
- Her key strengths include training and coaching team members, maintaining processes and documentation, and delivering excellent customer service.
El documento describe el modelo de evaluación de desempeño por competencias del CIEDI para los años 2009-2010. El modelo evalúa a los profesores en tres ámbitos: personal, pedagógico y profesional, y de procesos y operaciones. Cada ámbito incluye competencias clave específicas que son evaluadas por familias, compañeros, superiores y un autoexamen, con el fin de mejorar el desempeño y desarrollar las habilidades requeridas.
Innovaciones tecnológicas de la informáticaamiguita10
El documento habla sobre las innovaciones tecnológicas de la informática. Menciona que la materia es informática, el maestro es Omar Martínez Cariño, la alumna es Cruz Areli López Ramírez y cursa el 3er grado del grupo A en el ciclo escolar 2013-2014 en la Escuela Secundaria Fray Bartolomé de las Casas en Acatlán de Osorio, Puebla.
This document discusses face recognition technology. It begins with an abstract stating that face recognition is the identification of humans by unique facial characteristics. It then discusses how face recognition works by identifying distinguishing facial features from images and comparing them to stored data. The document then provides an introduction to biometrics and how face recognition can be used for applications like criminal identification. It describes different face recognition algorithms and provides summaries of several research papers on face recognition techniques.
Innovative Analytic and Holistic Combined Face Recognition and Verification M...ijbuiiir1
Automatic recognition and verification of human faces is a significant problem in the development and application of Human Computer Interaction (HCI).In addition, the demand for reliable personal identification in computerized access control has resulted in an increased interest in biometrics to replace password and identification (ID) card. Over the last couple of years, face recognition researchers have been developing new techniques fuelled by the advances in computer vision techniques, Design of computers, sensors and in fast emerging face recognition systems. In this paper, a Face Recognition and Verification System has been designed which is robust to variations of illumination, pose and facial expression but very sensitive to variations of the features of the face. This design reckons in the holistic or global as well as the analyticor geometric features of the face of the human beings. The global structure of the human face is analysed by Principal Component Analysis while the features of the local structure are computed considering the geometric features of the face such as the eyes, nose and the mouth. The extracted local features of the face are trained and later tested using Artificial Neural Network (ANN). This combined approach of the global and the local structure of the face image is proved very effective in the system we have designed as it has a correct recognition rate of over 90%.
Cross Pose Facial Recognition Method for Tracking any Person's Location an Ap...ijtsrd
In todays world, there are number of existing methods for facial recognition. These methods are based on frontal view face data. There are few methods which are based on non-frontal view face recognition method. In most of the face recognition algorithm, œFeature space approach is used. In this approach, different feature vectors are extracted from face. These distances are compared to determine matches. In this paper, it is proposed that how any person can be located in a campus or in a city using a cross pose face recognition method. This paper is focusing on three parts 1) generation of multi-view images 2) comparison of images 3) showing the actual location of a person. Sanjay D. Sawaitul"Cross Pose Facial Recognition Method for Tracking any Persons Location an Approach" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7186.pdf http://www.ijtsrd.com/computer-science/data-processing/7186/cross-pose-facial-recognition-method-for--tracking-any-persons-location-an-approach/sanjay-d-sawaitul
HUMAN FACE RECOGNITION USING IMAGE PROCESSING PCA AND NEURAL NETWORKijiert bestjournal
Security and authentication of a person is a vital part of any business. There are many techniques use d for this purpose. One of technique is human face recognition . Human Face recognition is an effective means of authenticating a person. The benefit of this approa ch is that,it enables us to detect changes in the face pattern of an individual to substantial extent. The recognition s ystem can tolerate local variations in the face exp ression of an individual. Hence Human face recognition can be use d as a key factor in crime detection mainly to iden tify criminals. There are several approaches to Human fa ce recognition of which Image Processing Principal Component Analysis (PCA) and Neural Networks have been includ ed in our project. The system consists of a databas e of a set of facial patterns for each individual. The charact eristic features called �eigenfaces� are extracted from the stored images using which the system is trained for subseq uent recognition of new images.
Facial recognition systems use computer algorithms to identify or verify people from digital images or video by analyzing patterns in their faces. The document traces the development of these systems from early work in the 1970s to modern applications. It describes different types of facial recognition techniques and provides examples of software using the technology. The document also summarizes the results of an online survey about public awareness and interest in using facial recognition. It concludes by noting improvements in accuracy over time but also ongoing challenges regarding error rates, privacy, and changes to facial features.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
1. The document discusses face recognition using an eigenface approach, which uses principal component analysis to extract features from a database of faces to generate eigenfaces that can be used to identify unknown faces.
2. The eigenface approach takes into account the entire face for recognition and is relatively insensitive to small changes in faces. It is faster, simpler, and has better learning capabilities compared to other approaches.
3. Some limitations are that accuracy is affected if lighting and face position vary greatly, it only works with grayscale images, and noisy or partially occluded faces decrease recognition performance.
This document discusses the history and development of facial recognition systems. It describes how pioneers in the 1960s began developing early systems using graphics tablets, and the challenges of accounting for variability in lighting, expression, and other factors. The document outlines different types of current facial recognition approaches, including traditional 2D recognition and emerging 3D recognition techniques. It provides examples of software using facial recognition and potential applications that have been developed or could be developed. A survey of Hong Kong citizens found facial recognition is not very common but many would be interested in using it on computers or for access control. The conclusion discusses both benefits and privacy concerns of the technology.
This document provides a literature review of face recognition techniques using face alignment and PCA. It discusses how face alignment techniques like Active Appearance Models (AAM) and Active Shape Models (ASM) are used to accurately align faces, which is important for face recognition. PCA is also discussed as a commonly used feature extraction and dimensionality reduction technique for face recognition. The document surveys recent research on face recognition using AAM for tasks like minimizing error between input and model images, modeling a wide range of facial appearances, and exploiting temporal correlations across image frames. It also discusses improvements to AAM modeling and fitting robustness.
CDS is the criminal face identification by capsule neural network.
Solving the common problems in image recognition such as illumination problem, scale variability, and to fight against a most common problem like pose problem, we are introducing Face Reconstruction System.
Techniques for Face Detection & Recognition Systema Comprehensive ReviewIOSR Journals
Abstract: Face detection and Facial recognition technology has emerged as a striking solution to address
many contemporary prerequisites for identification and the verification of identity prerogatives. It brings
together the potential of supplementary biometric systems, which attempt to link identity to individually
distinctive features of the body, and the more acquainted functionality of visual surveillance systems. In current
decades face recognition has experienced significant consideration from both research communities and the
marketplace, conversely still remained very electrifying in real applications. The assignment of face detection
and recognition has been dynamically researched in current eternities. This paper offers a conversant
evaluation of foremost human face recognition research. We first present a summary of face detection, face
recognition and its solicitations. Then, a literature review of the predominantly used face recognition techniques
is accessible.
Clarification and restrictions of the performance of these face recognition algorithms are specified.
Here we present a vital assessment of the current researches concomitant with the face recognition process. In
this paper, we present a broad range review of major researches on face recognition process based on various
circumstances. In addition, we present a summarizing description of Face detection and recognition process
and development along with the techniques connected with the various influences that affects the face
recognition process.
Keywords: Face Detection, Face Recognition System, Biometric System, Review Research.
Techniques for Face Detection & Recognition Systema Comprehensive ReviewIOSR Journals
This document provides a comprehensive review of techniques for face detection and recognition systems. It begins with an abstract that outlines face detection and recognition technology and its use in identification and verification. The introduction discusses the challenges of automatic face recognition compared to human face recognition abilities. Section II reviews recent face detection techniques, including feature-based and image-based approaches. Section III discusses unsupervised classification-based approaches for face recognition, including Eigenfaces, dynamic graph matching, and geometrical feature matching. Section IV addresses intelligent supervised approaches like neural networks and support vector machines. The conclusion compares different face databases and provides an overall assessment of current face recognition research.
Automatic Attendance Management System Using Face RecognitionKathryn Patel
1) The document describes an automatic attendance management system using face recognition. It uses image processing and facial recognition techniques to take attendance digitally.
2) The system works by using a camera to take photos of students' faces and comparing them to a database of registered student photos using principal component analysis. It aims to make attendance taking less time-consuming and manipulable than traditional paper-based systems.
3) The system consists of a camera, microcontroller, and MATLAB software. The camera captures photos and sends them to MATLAB for facial recognition using eigenfaces. It then marks the attendance automatically.
ADVANCED FACE RECOGNITION FOR CONTROLLING CRIME USING PCAIAEME Publication
Face recognition has been a rapidly creating, testing and fascinating area with
respect to consistent applications. The task of face acknowledgment has been viably
asked about lately. With data and information gathering in abundance, there is an
urgent necessity for high security. Face acknowledgment has been a rapidly creating,
testing and interesting area concerning persistent applications. This paper gives a
cutting edge review of critical human face acknowledgment investigate
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Face detection is one of the most suitable applications for image processing and biometric programs. Artificial neural networks have been used in the many field like image processing, pattern recognition, sales forecasting, customer research and data validation. Face detection and recognition have become one of the most popular biometric techniques over the past few years. There is a lack of research literature that provides an overview of studies and research-related research of Artificial neural networks face detection. Therefore, this study includes a review of facial recognition studies as well systems based on various Artificial neural networks methods and algorithms.
Attendance System using Face RecognitionIRJET Journal
This document describes an automated attendance system using face recognition. It discusses using algorithms like Viola-Jones for face detection and PCA for feature extraction and SVM for classification. The system works by capturing images of students' faces with a camera as they enter the classroom. It then detects faces, recognizes the students, and automatically marks their attendance on an attendance sheet. The system is presented as an improvement over previous biometric-based attendance systems in that it is faster, more convenient, and helps monitor students.
IRJET- A Review on Various Approaches of Face RecognitionIRJET Journal
This document reviews various approaches for face recognition. It begins by describing challenges in face recognition related to scale, pose, illumination, and disguise. It then discusses principal component analysis (PCA) and local discriminant analysis (LDA), which are appearance-based approaches, as well as local binary pattern (LBP) and local ternary pattern (LTP), which are texture-based approaches. PCA uses eigenfaces to represent facial features while LDA aims to preserve discriminating information between classes. LBP and LTP extract texture features from facial images for recognition. The document concludes LDA generally provides better accuracy than PCA for whole-face recognition, while LTP performs better than other methods for texture-based recognition as it is more
This report is based on research. This whole research content are taken by books and websites. you can learn about face recognition history, how's it is work traditional and in technical way, introduction of some face recognition software and devices. we also add face recognition algorithm in report.
Development of Real Time Face Recognition System using OpenCVIRJET Journal
1) The document describes the development of a real-time face recognition system using OpenCV.
2) The system detects faces in images from a webcam in real-time, extracts 128 features from each face using a deep neural network, and recognizes faces by comparing to stored models using a support vector machine classifier.
3) The system provides a graphical user interface and was developed using open source tools like OpenCV, OpenFace, Python, etc. to allow real-time face detection and recognition.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...alexjohnson7307
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Trusted Execution Environment for Decentralized Process MiningLucaBarbaro3
Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframePrecisely
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...Tatiana Kojar
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
1. MandeepkaurSandhuand, Ajay Kumar Dogra / International Journal of Engineering Research
and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue4, July-august 2012, pp.1322-1328
Metamorphism using Warping and Vectorization Method for
Image:Review
MandeepkaurSandhuand Ajay Kumar Dogra.
Department of Computer Science Engineering, Beant college of Engineering and Management, Gurdaspur.
Abstract:
Face recognition presents a challenging difficultly government buildings, and ATMs (automatic teller
in the field of image analysis and computer vision, machines), and to secure computers and mobile
and as such has received a great deal of devotion phones.Computerized facial recognition is based on
over the last few years because of its many tenders capturing an image of a face, extracting features,
in various domains. In this paper, an overview of comparing it to images in a database, and identifying
some of the well-known methods. The applications matches. As the computer cannot see the same way
of this technology and some of the difficulties as a human eye can, it needs to convert images into
plaguing current systems with regard to this task numbers representing the various features of a face.
have also been provided. This paper also mentions The sets of numbers representing one face are
some of the most recent algorithms developed for compared with numbers representing another face.
this purpose and attempts to give an idea of the The excellence of the computer recognition system is
state of the art of face recognition technology. hooked onthe quality of the image and mathematical
algorithms used to convert a picture into numbers.
Keywords: Face recognition, biometric, algorithms Important factors for the image excellence are light,
background, and position of the head. Pictures can be
INTRODUCTION: taken of a still or moving subjects. Still subjects are
Interest in face recognition is moving toward photographed, for example by the police (mug shots)
uncontrolledor moderately controlled environments, or by specially placed security cameras (access
in that either theprobe or gallery images or both are control). However, the most challenging application
assumed to be acquiredunder uncontrolled is the ability to use images captured by surveillance
conditions. Also of interest are morerobust similarity cameras (shopping malls, train stations, ATMs), or
measures or, in general, techniques todetermine closed-circuit television (CCTV). In many cases the
whether two facial images correctly match, subject in those images is moving fast, and the light
i.e.,whether they belong to the same person.An and the position of the head is not optimal.
important real-life application of interest is Face Recognition
automatedsurveillance, where the objective is to Challenges
recognize and trackpeople who are on a watch list. In Physical appearance
this open world applicationthe system is tasked to Acquisition geometry
recognize a small set of people whilerejecting Imaging conditions
everyone else as being one of the wanted Compression artifacts
people.Traditionally, algorithms have been developed 1. Face Detection
for closedworld applications. Theassumption is that Face detection task: to identify and locate
the probe image and its closest image inthe gallery human faces in an image regardless of their
belong to the same person. The information obtained position, scale, in plane rotation, orientation,
by this search is then used to dorecognition. pose (out of plane rotation), and
Currently, the technology is used by police, forensic illumination.
scientists, governments, private companies, the The first step for any automatic face
military, and casinos. The police use facial recognition system
recognition for identification of criminals. Face detection methods:
Companies use it for securing access to restricted
areas. Casinos use facial recognition to eliminate Knowledge-based: Encode human knowledge of
Cheaters and dishonest money counters. Finally, in what constitutes a typical face (usually, the
the United States, nearly half of the states use relationship between facial features)
computerized identity verification, while the National
Center for Missing and Exploited Children uses the Feature invariant approaches: Aim to find
technique to find missing children on the Internet. In structural features of a face that exist even when the
Mexico, a voter database was compiled to prevent pose, viewpoint, or lighting conditions vary
vote fraud. Facial recognition technology can be used
in a number of other places, such as airports,
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2. MandeepkaurSandhuand, Ajay Kumar Dogra / International Journal of Engineering Research
and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue4, July-august 2012, pp.1322-1328
Template matching methods:Several standard Face Recognition
patterns stored to describe the face as a whole or the In general, face recognition systems proceed
facial features separately. The most direct method by identifying the face in the scene, thus estimating
used for face recognition is the matching between and normalizing for translation, scale, and in-plane
the test images and a set of training images rotation. Many approaches to finding faces are based
based on measuring the correlation. The similarity on weak models of the human face that model face
is obtained by normalize cross correlation. shape in terms of facial texture.
Once a prospective face has been localized, the
Appearance-based methods:The models (or approaches to face recognition then divided into two
templates) are learned from a set of training images categories:
which capture the representative variability of facial Face appearance
appearance Face geometry
Framework of Face recognition
Face Region/
position of facial
feature
Face Detection
Find
Face Recognition
1. Face Region
Identify the person
2. Facial
Feature
Face Recognition: Advantages
Photos of faces are widely used in passports Authentication systems. This is only a first
and driver’s licenses where the possession challenge in a long list of technical
authentication protocol is augmented with a challenges that are associated with robust
photo for manual inspection purposes; there face authentication
is wide public acceptance for this biometric Face currently is a poor biometric for use in
identifier a pure identification protocol
An obvious circumvention method is
disguise
Face recognition systems are the least There is some criminal association with face
intrusive from a biometric sampling point of identifiers since this biometric has long been
view, requiring no contact, nor even the used by law enforcement agencies
awareness of the subject (‘mugshots’).
The biometric works, or at least works in 2. Face Recognition Algorithms
theory, with legacy photograph data-bases, 3.1 Principal Component Analysis (PCA)
videotape, or other image sources Derived from Karhunen-Loeve's
Face recognition can, at least in theory, be transformation. Given an s-dimensional vector
used for screening of unwanted individuals representation of each face in a training set of
in a crowd, in real time images, Principal Component Analysis (PCA) tends
It is a fairly good biometric identifier for to find a t-dimensional subspace whose basis vectors
small-scale verification applications correspond to the maximum variance direction in the
Face Recognition: Disadvantages original image space. This new subspace is normally
lower dimensional (t<<s). If the image elements are
A face needs to be well lighted by controlled considered as random variables, the PCA basis
light sources in automated face vectors are defined as eigenvectors of the scatter
matrix.
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Vol. 2, Issue4, July-august 2012, pp.1322-1328
Principal component analysis (PCA) is a data in a way which finestdescribes the variance in
mathematical procedure that uses an orthogonal the data. If a multivariate dataset is pictured as a set
transformation to convert a set of observations of of coordinates in a high-dimensional data space (1
possibly correlated variables into a set of values of axis per variable), PCA can supply the user with a
linearly uncorrelated variables called principal lower-dimensional picture, a "shadow" of this object
components. The number of principal components is when viewed from its (in some sense) most
less than or equal to the number of original variables. informative viewpoint. This is done by using only the
This transformation is defined in such a way that the first few principal components so that the
first principal component has the dimensionality of the transformed data is reduced.
majorconceivablevariance (that is, accounts for as PCA is meticulously related to factor analysis. Factor
much of the variability in the data as possible), and analysis normally incorporates more domain specific
each following component in turn has the highest suppositions about the underlying structure and
variance possible under the constraint that it be solves eigenvectors of a slightly different matrix.
orthogonal to (i.e., uncorrelated with) the preceding
components. Principal components are guaranteed to 3.2 Independent Component Analysis (ICA)
be independent only if the data set is jointly normally Independent Component Analysis (ICA)
distributed. PCA is sensitive to the relative scaling of minimizes both second-order and higher-order
the original variables. Depending on the field of dependencies in the input data and attempts to find
application, it is also named the discrete Karhunen– the basis along which the data (when projected onto
Loève transform (KLT), the Hotelling transform or them) are - statistically independent. Bartlett et al.
proper orthogonal decomposition (POD). provided two architectures of ICA for face
PCA was invented in 1901 by Karl Pearson recognition task: Architecture I - statistically
[6].Now it is mostly used as a tool in exploratory data independent basis images, and Architecture II -
analysis and for creatingpredictive models. PCA can factorial code representation. Independent component
be done by eigenvalue decomposition of a data analysis (ICA) is a computational method from
covariance (or correlation) matrix or singular value statistics and signal processing which is a special
decomposition of a data matrix, usually after mean case of blind source separation. ICA seeks to separate
centering (and normalizing or using Z-scores) the a multivariate signal into additive subcomponents
data matrix for each attribute[7]. The results of a supposing the mutual statistical independence of the
PCA are usually discussed in terms of component non-Gaussian source signals. The general framework
scores, sometimes called factor scores (the of ICA was introduced in the early 1980s (Hérault
transformed variable values corresponding to a and Ans 1984; Ans, Hérault and Jutten 1985; Hérault,
particular data point), and loadings (the weight by Jutten and Ans 1985), but was most clearly stated by
which each standardized original variable should be Pierre Comon in 1994 (Comon1994). For a good text,
multiplied to get the component score)[7]. see Hyvärinen, Karhunen and Oja (2001).ICA finds
PCA is the simplest of the true eigenvector-based the independent components (aka factors, latent
multivariate analyses. Often, its operation can be variables or sources) by maximizing the statistical
thought of as revealing the internal structure of the independence of the estimated components.
Fig:1 Represents PCA method
We may choose one of many ways to define the ICA algorithms. The two broadest definitions of
independence, and this choice governs the form of independence for ICA are
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4. MandeepkaurSandhuand, Ajay Kumar Dogra / International Journal of Engineering Research
and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue4, July-august 2012, pp.1322-1328
JADE, but there are many others.In general, ICA
1) Minimization of Mutual Information cannot identify the actual number of source signals, a
2) Maximization of non-Gaussianity uniquely correct ordering of the source signals, nor
The Minimization-of-Mutual information (MMI) the proper scaling (including sign) of the source
family of ICA algorithms uses measures like signals. ICA is important to blind signal separation
Kullback-leiber Divergence and maximum-entropy. and has many practical applications. It is closely
The Non-Gaussianity family of ICA algorithms, related to (or even a special case of) the search for a
motivated by the central limit theorem, uses kurtosis factorial code of the data, i.e., anew vector-valued
and Negentrogy. Typical algorithms for ICA use representation of each data vector such that it gets
centering, whitening (usually with the eigenvalue uniquely encoded by the resulting code vector (loss-
decomposition), and dimensionality reduction as free coding), but the code components are
preprocessing steps in order to simplify and reduce statistically independent.
the complexity of the problem for the actual iterative Comparison of PCA and ICA
algorithm. Whitening and dimension reduction can be PCA
achieved with principal component analysis or – Focus on uncorrelated and Gaussian components
singular value decomposition [2]. – Second-order statistics
Independent component analysis (ICA) is a – Orthogonal transformation
computational method for separating a multivariate
signal into additive subcomponents supposing the ICA
mutual statistical independence of the non-Gaussian – Focus on independent and non-Gaussian
source signals. It is a special case of separation. components
Whitening ensures that all dimensions are treated – Higher-order statistics
equally a priori before the algorithm is run. – Non-orthogonal transformation
Algorithms for ICA include infomax, FastICA, and
We do not know
Both unknowns
Some o
ptimizatio
n
Function is require
d!!
categorize among classes. For all samples of all
3.3 Linear Discriminant Analysis (LDA) classes thebetween-class scatter matrix SBand the
Linear Discriminant Analysis (LDA) finds within-class scatter matrix SWare defined.The goal is
the vectors in the underlying space that best to maximize SBwhile minimizing SW, in other
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5. MandeepkaurSandhuand, Ajay Kumar Dogra / International Journal of Engineering Research
and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue4, July-august 2012, pp.1322-1328
words, maximize the ratio det|S|/det|SW.
This ratio is maximized when the column vectors of N
the projection matrix are the eigenvectors of (SW^-1 2. SB= ∑ ni(mi-m)(mi-m)T
× SB).Linear discriminant analysis (LDA) and the i=l
related Fisher's linear discriminantare dimensionality whereniis the number of images in the class, mi is the
before later classification.LDA is closely linked to mean of the images in the class and m is the mean of
ANOVA (analysis of variance) and regressionwhich all the images.
also attempt to express one dependent variable as a 3.Solve the generalized eigenvalue problem
linear combination of other structures or SBV = ᶺSWV
extents[10][3].In the other two methods however, the
dependent variable is a numerical quantity, while for 3.4 Hidden Markov Models (HMM)
LDA it is a categorical variable (i.e. the class label). Hidden Markov Models (HMM) are a set of
Logistic regression andprobitregression are more statistical models used to characterize the statistical
similar to LDA, as they also explain a categorical properties of a signal. HMM consists of two
variable. These other methods are preferable in interrelated processes:
applications where it is not reasonable to assume that (1) An underlying, unobservable Markov chain with
the independent variables are normally distributed, a finite number of states, a state transition probability
which is a fundamental assumption of the LDA matrix and an initial state probability distribution and
method.LDA is also closely related to principal (2)A set of probability density functions associated
component analysis (PCA) and factor analysis in that with each state.
they both look for linear combinations of variables
which best explain the data [3]. LDA explicitly A Hidden Markov Models (HMM) is a finite
attempts to model the difference between the classes state machine which has some fixed number of states.
of data. PCA on the other hand does not take into It provides a probabilistic framework for modeling a
account any difference in class, and factor analysis time series of multivariate observations.
builds the feature combinations based on differences Hidden Markov models were introduced in the
rather than similarities. Discriminant analysis is also beginning of the 1970‟s as a tool in speech
different from factor analysis in that it is not an recognition. This model based on statistical methods
interdependence technique: a distinction between has become progressively popular in the last several
independent variables and dependent variables (also years due to its strong mathematical structure and
called criterion variables) must be made.LDA works theoretical basis for use in a wide range of
when the measurements made on independent applications [1].
variables for each observation are continuous A hidden Markov model is a doubly
quantities. When dealing with categorical stochastic process, with an underlying stochastic
independent variables, the equivalent technique is process that is not observable (hence the word
discriminant correspondence analysis[7][8]. hidden), but can be observed through another
stochastic process that creates the sequence of
Basic steps of LDA algorithm [3] observations. The hidden process consists of a set of
LDA uses PCA subspace as input data, i.e., conditions connected to each other by changes with
matrix V obtained from PCA. The advantage is probabilities, while the observed process consists of a
cutting the eigenvectors in matrix V that are not set of outputs or observations, each of which may be
important for face recognition (this significantly emitted by each state according to some probability
improves computing performance). LDA considers density function (pdf). Depending on the nature of
between and also within class correspondence of this pdf, several HMM classes can be distinguished.
data. It means that training images create a class for If the observations are naturally discrete or quantized
each subject, i.e., one class = one subject (all his/her using vector quantization [11].
training images).
3.5Face recognition using line edge map (LEM)
1.Determine LDA subspace (i.e. determining the line This algorithm describes a new technique
in Fig. 2) from training data. Calculate the within based on line edge maps (LEM) to accomplish face
class scatter matrix recognition. In addition, it proposes a line matching
c technique to mark this task possible. In opposition
with other algorithms, LEM uses physiologic features
Sw∑i ,si= ∑(x-mi) (x-mi)T
i=lx€xi
from human faces to solve the problem; it mainly
uses mouth, nose and eyes as the most characteristic
wheremiis the mean of the images in the class and C
ones.
is the number of classes.Calculate the between class
scatter matrix
In order to degree the similarity of human faces the
face images are initially converted into gray-level
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6. MandeepkaurSandhuand, Ajay Kumar Dogra / International Journal of Engineering Research
and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue4, July-august 2012, pp.1322-1328
pictures. The images are encoded into binary edge certain parameter settings LDA produced the worst
maps using Sobel edge detection algorithm. This recognition rates from among all experiments.
system is muchrelated to the way human beings Experiments with their proposed methods
perceive other people faces as it was stated in many PCA+SVM and LDA+SVM produced a better
psychological studies. The main advantage of line maximum recognition rate than traditional PCA and
edge maps is the low sensitiveness to illumination LDA methods. Combination LDA+SVM produced
changes, because it is an intermediate-level image more consistent results than LDA alone. Altogether
representation derived from low-level edge map they made more than 300 tests and achieved
representation.[9] The algorithm has another maximum recognition rates near 100% (LDA+SVM
important improvement, it is the low memory once actually 100%)[8].Chengjun Liu and Harry
requirements because the sensible of data used. In Wechsler address the relative usefulness of the
there is an example of a face line edge map; it can be independentcomponent analysis for Face
observed that it keeps face features but in a very Recognition. The sensitivity analysis suggests that for
abridgedlevel [13]. enhanced performance ICA should be carried out in a
compressed and whitened space where most of the
characteristic information of the original data is
conserved and the small trailing eigenvalues
unwanted. The dimensionality of the compressed
subspace is decided founded on the eigenvalue
spectrum from the training data. In the result their
discriminant analysis shows that the ICA criterion,
when carried out in the properly compressed and
whitened space, performs better than the eigenfaces
and Fisherfaces methods for face recognition[4].
5. Conclusions:
Face recognition is a challenging problem in
the field ofimage analysis and computer vision that
has received a great deal of attention over the last few
years because of its many applications in various
4. Face Recognition Techniques domains. Research has been conducted dynamically
Taranpreet Singh Ruprahhas presented a in this area for the past four agesor so, and though
face recognition systemusing PCA with neural huge progress has been made, encouraging results
networks in the context of face verification and face have been obtained and current face recognition
recognition using photometric normalization for systems have reached a certain degree of maturity
association. The experimental results show the N.N. when operating under constrained conditions;
Euclidean distance rules using PCA for overall however, they are far from achieving the ideal of
presentation for verification. However, for being able to perform adequately in all the various
recognition, E.D.classifier gives the highest accuracy situations that are commonly encountered by
using the original face image. Thus, applying applications utilizing these techniques in practical
histogram equalization techniques on the face image life.
do not give much impact to the performance of the
system if conducted under controlled 6. References:
environment[17].Byongjoo Ohproposes a face [1] A. El-Yacoubi, M. Gilloux,R. Sabourin,
recognition algorithmand presented results of Member, IEEE, and C.Y. Suen, Fellow,
performance test on the ORL database. The PCA IEEE , An HMM-Based Approach for Off-
algorithm is first tried as a reference performance. Line Unconstrained Handwritten Word
Then PCA+MLNN algorithm was proposed and Modeling and Recognition,IEEE
tested in the same environments. The PCA+ MLNN Transactions On Pattern Analysis And
algorithm shows better performance, and resulted Machine Intelligence,21(8),1999.
in95.29% recognition rate. The introduction of [2] A.Hyvärinen, J.Karhunen and
MLNN enhances the classification performance and E.Oja,Independent Component Analysis,
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show, LDA alone is not suitable for practical use. At Recognition,Appears in the Second
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