Ioannis Pitas, Professor, Aristotle University of Thessaloniki, Department of Informatics (IEEE Fellow), Semantic 3DTV Content Analysis and Description
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.
Robust 3 d face recognition in presence ofijfcstjournal
In this paper, we propose a robust 3D face recognition system which can handle pose as well as occlusions
in real world. The system at first takes as input, a 3D range image, simultaneously registers it using
ICP(Iterative Closest Point) algorithm. ICP used in this work, registers facial surfaces to a common model
by minimizing distances between a probe model and a gallery model. However the performance of ICP
relies heavily on the initial conditions. Hence, it is necessary to provide an initial registration, which will
be improved iteratively and finally converge to the best alignment possible. Once the faces are registered,
the occlusions are automatically extracted by thresholding the depth map values of the 3D image. After the
occluded regions are detected, restoration is done by Principal Component Analysis (PCA). The restored
images, after the removal of occlusions, are then fed to the recognition system for classification purpose.
Features are extracted from the reconstructed non-occluded face images in the form of face normals. The
experimental results which were obtained on the occluded facial images from the Bosphorus 3D face
database, illustrate that our occlusion compensation scheme has attained a recognition accuracy of
91.30%.
1) The document presents a new face parts detection algorithm that combines the Viola-Jones object detection framework with geometric information of facial features.
2) It detects faces, then isolates regions of interest for the eyes, nose, and mouth. Eye pupils are located using iris recognition techniques.
3) The algorithm was tested on hundreds of images and showed promising results for automated facial feature detection.
Face recognition across non uniform motion blur, illumination, and posePvrtechnologies Nellore
The document proposes a method for face recognition in the presence of non-uniform motion blur from hand-held cameras. It models a blurred face as a convex combination of geometrically transformed gallery images. It develops an algorithm using the assumption of sparse camera motion and an l1-norm constraint. The framework is extended to handle illumination variations by exploiting the bi-convex set of images from blurring and illumination changes. The method is also extended to account for pose variations and uses a multi-scale implementation for efficient computation and memory usage.
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.
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.
This document discusses the implementation of the Scale Invariant Feature Transform (SIFT) algorithm in various applications, including face recognition, iris recognition, fingerprint recognition, and real-time hand gesture recognition. It provides an overview of how SIFT extracts distinctive local feature descriptors from images that are invariant to changes in illumination, rotation, scaling, and other transformations. The document then examines in more detail how SIFT has been applied and sometimes modified for use in each of these application areas, highlighting both its effectiveness and some limitations.
Fully Automatic Facial Feature Point Detection Using Gabor Feature Based Boos...Yen Ho
This is a key paper : Fully Automatic Facial Feature Point Detection Using Gabor Feature Based Boosted Classifiers - face detection (100%) & feature extraction(93%) for expressionless faces
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.
Robust 3 d face recognition in presence ofijfcstjournal
In this paper, we propose a robust 3D face recognition system which can handle pose as well as occlusions
in real world. The system at first takes as input, a 3D range image, simultaneously registers it using
ICP(Iterative Closest Point) algorithm. ICP used in this work, registers facial surfaces to a common model
by minimizing distances between a probe model and a gallery model. However the performance of ICP
relies heavily on the initial conditions. Hence, it is necessary to provide an initial registration, which will
be improved iteratively and finally converge to the best alignment possible. Once the faces are registered,
the occlusions are automatically extracted by thresholding the depth map values of the 3D image. After the
occluded regions are detected, restoration is done by Principal Component Analysis (PCA). The restored
images, after the removal of occlusions, are then fed to the recognition system for classification purpose.
Features are extracted from the reconstructed non-occluded face images in the form of face normals. The
experimental results which were obtained on the occluded facial images from the Bosphorus 3D face
database, illustrate that our occlusion compensation scheme has attained a recognition accuracy of
91.30%.
1) The document presents a new face parts detection algorithm that combines the Viola-Jones object detection framework with geometric information of facial features.
2) It detects faces, then isolates regions of interest for the eyes, nose, and mouth. Eye pupils are located using iris recognition techniques.
3) The algorithm was tested on hundreds of images and showed promising results for automated facial feature detection.
Face recognition across non uniform motion blur, illumination, and posePvrtechnologies Nellore
The document proposes a method for face recognition in the presence of non-uniform motion blur from hand-held cameras. It models a blurred face as a convex combination of geometrically transformed gallery images. It develops an algorithm using the assumption of sparse camera motion and an l1-norm constraint. The framework is extended to handle illumination variations by exploiting the bi-convex set of images from blurring and illumination changes. The method is also extended to account for pose variations and uses a multi-scale implementation for efficient computation and memory usage.
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.
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.
This document discusses the implementation of the Scale Invariant Feature Transform (SIFT) algorithm in various applications, including face recognition, iris recognition, fingerprint recognition, and real-time hand gesture recognition. It provides an overview of how SIFT extracts distinctive local feature descriptors from images that are invariant to changes in illumination, rotation, scaling, and other transformations. The document then examines in more detail how SIFT has been applied and sometimes modified for use in each of these application areas, highlighting both its effectiveness and some limitations.
Fully Automatic Facial Feature Point Detection Using Gabor Feature Based Boos...Yen Ho
This is a key paper : Fully Automatic Facial Feature Point Detection Using Gabor Feature Based Boosted Classifiers - face detection (100%) & feature extraction(93%) for expressionless faces
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.
This project exposes the implementation of an iris based biometric system, from the theoretical basis to the implementation of it by examining different types of methods described in other documents.
The human iris structure remains invariant over the time containing several easily identifiable structures, believed to be unique to each person. This information is extracted by using mathematical pattern-recognition techniques to obtain a characteristic iris code which can be used in identification systems.
The recognition principle is the failure of a test of statistical independence on the iris codes since two different iris codes should not agree in more than a half of their bits. The operating principle is as follows: first the system has to localize the inner and outer boundaries of the iris (pupil and limbus) in an image of an eye. Further subroutines detect and exclude eyelids, eyelashes, and specular reflections that often occlude parts of the iris. The set of pixels containing only the iris, normalized by a rubber-sheet model to compensate for pupil dilation or constriction, is then analyzed to extract an iris code encoding the information needed to compare two iris images. The code generated by imaging an iris is compared to stored template(s) in a database. If the Hamming distance is below the decision threshold, a positive identification outcomes due to the statistical improbability that two different persons could agree by chance in so many bits, given the high entropy of iris templates.
The iris segmentation and normalization process is challenging due to the presence of eyelashes, eyelids, and reflections that may occlude regions of the iris, furthermore, the dilation of pupils due to different light illuminations and the inconvenient that the iris and the pupil are not concentric cause this type of biometry to be quite complex.
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.
A facial recognition system automatically identifies or verifies a person from images or video by comparing their facial features to a database. It started being researched in the 1960s and is now used for security systems. Early 2D systems had low accuracy due to lighting and expressions, while newer 3D systems can recognize faces from different angles unaffected by these factors. Facial recognition involves image acquisition, pre-processing, feature extraction to describe the face, classification of expressions, and post-processing. Challenges include pose, environment clutter, illumination, and facial variability between individuals. More research is still needed to develop robust systems unaffected by data variability.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
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 document describes an algorithm for eye detection in face images. It begins with face detection using skin color detection in HSV color space. Then it finds the symmetric axis of the extracted face region using gradient orientation histograms to determine the location of the eyes. It further finds the symmetric axis within the eye region to locate the center of the eyes. The algorithm aims to accurately detect the eyes even when the face is rotated, which is important for applications like face recognition and gaze tracking.
This document summarizes a research paper on face recognition. It discusses what face recognition is, how it works through face detection and recognition. It describes different approaches to face recognition including feature extraction methods, holistic methods, and hybrid methods. It discusses problems with face recognition related to variations in expressions, makeup, lighting. It provides examples of applications of face recognition technology including access control systems, time attendance tracking, and facial recognition software for online gaming and crime prevention.
1. The document discusses gait recognition from video for biometric identification. It provides background on biometric recognition and discusses gait as an identifying biometric trait that can be captured from a distance.
2. Various research approaches to gait recognition are covered, including model-based, motion-based, and mixed approaches. Commonly used gait recognition databases are also listed.
3. Recent works applying techniques like matrix representations, Bayesian frameworks, and symmetry-based detection are summarized, demonstrating applications in human identification, activity recognition, and scene registration. Future directions discussed include improving performance under more natural conditions.
This document discusses face biometrics and face recognition systems. It explains that there are two types of biometrics: physiological and behavioral characteristics. Physiological biometrics include finger scans, iris scans, retina scans, hand scans, and facial recognition. The document then focuses on facial recognition, noting that it analyzes 80 landmarks on the human face. It describes two types of facial recognition comparisons: verification and identification. The document explains that facial recognition is preferable to other biometrics because it requires no physical interaction and does not require an expert to interpret results. It provides an overview of how facial recognition systems work in three steps: loading a photo, detecting faces, and recognizing faces using algorithms. The document concludes by describing several algorithms used for facial
The document discusses iris biometrics and an iris recognition system. It provides details on iris anatomy, image acquisition, preprocessing, iris localization including pupil and iris detection, iris normalization, feature extraction using Haar wavelets, and matching. It evaluates the system on three databases achieving over 94% accuracy with low false acceptance and rejection rates. Further work is proposed on fusion, dual extraction approaches, indexing large databases, and using local descriptors.
IRIS BIOMETRIC RECOGNITION SYSTEM EMPLOYING CANNY OPERATORcsitconf
Biometrics has become important in security applications. In comparison with many other
biometric features, iris recognition has very high recognition accuracy because it depends on
iris which is located in a place that still stable throughout human life and the probability to find
two identical iris's is close to zero. The identification system consists of several stages including
segmentation stage which is the most serious and critical one. The current segmentation
methods still have limitation in localizing the iris due to circular shape consideration of the
pupil. In this research, Daugman method is done to investigate the segmentation techniques.
Eyelid detection is another step that has been included in this study as a part of segmentation
stage to localize the iris accurately and remove unwanted area that might be included. The
obtained iris region is encoded using haar wavelets to construct the iris code, which contains
the most discriminating feature in the iris pattern. Hamming distance is used for comparison of
iris templates in the recognition stage. The dataset which is used for the study is UBIRIS
database. A comparative study of different edge detector operator is performed. It is observed
that canny operator is best suited to extract most of the edges to generate the iris code for
comparison. Recognition rate of 89% and rejection rate of 95% is achieved.
This document presents a new approach for human identification using sclera recognition. It begins with background on sclera and challenges with sclera recognition. It then describes the proposed methodology which includes sclera segmentation, feature extraction using Gabor filtering, and recognition using Bayesian classification. Experimental results show the false accept and reject rates for the approach. It concludes that sclera recognition is promising for human identification and can achieve accuracy comparable to iris recognition in visible light. The proposed approach uses Bayesian classification for recognition, which is more effective than previous matching score methods.
A Spectral Domain Local Feature Extraction Algorithm for Face RecognitionCSCJournals
In this paper, a spectral domain feature extraction algorithm for face recognition is proposed, which efficiently exploits the local spatial variations in a face image. For the purpose of feature extraction, instead of considering the entire face image, an entropy-based local band selection criterion is developed, which selects high-informative horizontal bands from the face image. In order to capture the local variations within these high-informative horizontal bands precisely, a feature selection algorithm based on two-dimensional discrete Fourier transform (2D-DFT) is proposed. Magnitudes corresponding to the dominant 2D-DFT coefficients are selected as features and shown to provide high within-class compactness and high between-class separability. A principal component analysis is performed to further reduce the dimensionality of the feature space. Extensive experimentations have been carried out upon standard face databases and the recognition performance is compared with some of the existing face recognition schemes. It is found that the proposed method offers not only computational savings but also a very high degree of recognition accuracy.
The document proposes a unified framework for iris recognition that addresses challenges in unconstrained acquisition, robust matching, and privacy. It uses random projections and sparse representations to select good quality iris images, recognize iris patterns in a single step, and introduce cancelable templates for enhanced privacy without compromising security or recognition performance. Experimental results on public datasets demonstrate benefits of the proposed approach for robust and accurate iris recognition.
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.
Human Segmentation Using Haar-ClassifierIJERA Editor
Segmentation is an important process in many aspects of multimedia applications. Fast and perfect segmentation of moving objects in video sequences is a basic task in many computer visions and video investigation applications. Particularly Human detection is an active research area in computer vision applications. Segmentation is very useful for tracking and recognition the object in a moving clip. The motion segmentation problem is studied and reviewed the most important techniques. We illustrate some common methods for segmenting the moving objects including background subtraction, temporal segmentation and edge detection. Contour and threshold are common methods for segmenting the objects in moving clip. These methods are widely exploited for moving object segmentation in many video surveillance applications, such as traffic monitoring, human motion capture. In this paper, Haar Classifier is used to detect humans in a moving video clip some features like face detection, eye detection, full body, upper body and lower body detection.
Constantine Kotropoulos, Associate Professor, Aristotle University of Thessaloniki, Department of Informatics, Sparse and Low Rank Representations in Music Signal Analysis
Nicholas Kalouptsidis, Professor, National and Kapodistrian University of Athens, Department of Informatics and Telecommunications, Nonlinear Communications: Achievable Rates, Estimation, and Decoding
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.
This project exposes the implementation of an iris based biometric system, from the theoretical basis to the implementation of it by examining different types of methods described in other documents.
The human iris structure remains invariant over the time containing several easily identifiable structures, believed to be unique to each person. This information is extracted by using mathematical pattern-recognition techniques to obtain a characteristic iris code which can be used in identification systems.
The recognition principle is the failure of a test of statistical independence on the iris codes since two different iris codes should not agree in more than a half of their bits. The operating principle is as follows: first the system has to localize the inner and outer boundaries of the iris (pupil and limbus) in an image of an eye. Further subroutines detect and exclude eyelids, eyelashes, and specular reflections that often occlude parts of the iris. The set of pixels containing only the iris, normalized by a rubber-sheet model to compensate for pupil dilation or constriction, is then analyzed to extract an iris code encoding the information needed to compare two iris images. The code generated by imaging an iris is compared to stored template(s) in a database. If the Hamming distance is below the decision threshold, a positive identification outcomes due to the statistical improbability that two different persons could agree by chance in so many bits, given the high entropy of iris templates.
The iris segmentation and normalization process is challenging due to the presence of eyelashes, eyelids, and reflections that may occlude regions of the iris, furthermore, the dilation of pupils due to different light illuminations and the inconvenient that the iris and the pupil are not concentric cause this type of biometry to be quite complex.
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.
A facial recognition system automatically identifies or verifies a person from images or video by comparing their facial features to a database. It started being researched in the 1960s and is now used for security systems. Early 2D systems had low accuracy due to lighting and expressions, while newer 3D systems can recognize faces from different angles unaffected by these factors. Facial recognition involves image acquisition, pre-processing, feature extraction to describe the face, classification of expressions, and post-processing. Challenges include pose, environment clutter, illumination, and facial variability between individuals. More research is still needed to develop robust systems unaffected by data variability.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
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 document describes an algorithm for eye detection in face images. It begins with face detection using skin color detection in HSV color space. Then it finds the symmetric axis of the extracted face region using gradient orientation histograms to determine the location of the eyes. It further finds the symmetric axis within the eye region to locate the center of the eyes. The algorithm aims to accurately detect the eyes even when the face is rotated, which is important for applications like face recognition and gaze tracking.
This document summarizes a research paper on face recognition. It discusses what face recognition is, how it works through face detection and recognition. It describes different approaches to face recognition including feature extraction methods, holistic methods, and hybrid methods. It discusses problems with face recognition related to variations in expressions, makeup, lighting. It provides examples of applications of face recognition technology including access control systems, time attendance tracking, and facial recognition software for online gaming and crime prevention.
1. The document discusses gait recognition from video for biometric identification. It provides background on biometric recognition and discusses gait as an identifying biometric trait that can be captured from a distance.
2. Various research approaches to gait recognition are covered, including model-based, motion-based, and mixed approaches. Commonly used gait recognition databases are also listed.
3. Recent works applying techniques like matrix representations, Bayesian frameworks, and symmetry-based detection are summarized, demonstrating applications in human identification, activity recognition, and scene registration. Future directions discussed include improving performance under more natural conditions.
This document discusses face biometrics and face recognition systems. It explains that there are two types of biometrics: physiological and behavioral characteristics. Physiological biometrics include finger scans, iris scans, retina scans, hand scans, and facial recognition. The document then focuses on facial recognition, noting that it analyzes 80 landmarks on the human face. It describes two types of facial recognition comparisons: verification and identification. The document explains that facial recognition is preferable to other biometrics because it requires no physical interaction and does not require an expert to interpret results. It provides an overview of how facial recognition systems work in three steps: loading a photo, detecting faces, and recognizing faces using algorithms. The document concludes by describing several algorithms used for facial
The document discusses iris biometrics and an iris recognition system. It provides details on iris anatomy, image acquisition, preprocessing, iris localization including pupil and iris detection, iris normalization, feature extraction using Haar wavelets, and matching. It evaluates the system on three databases achieving over 94% accuracy with low false acceptance and rejection rates. Further work is proposed on fusion, dual extraction approaches, indexing large databases, and using local descriptors.
IRIS BIOMETRIC RECOGNITION SYSTEM EMPLOYING CANNY OPERATORcsitconf
Biometrics has become important in security applications. In comparison with many other
biometric features, iris recognition has very high recognition accuracy because it depends on
iris which is located in a place that still stable throughout human life and the probability to find
two identical iris's is close to zero. The identification system consists of several stages including
segmentation stage which is the most serious and critical one. The current segmentation
methods still have limitation in localizing the iris due to circular shape consideration of the
pupil. In this research, Daugman method is done to investigate the segmentation techniques.
Eyelid detection is another step that has been included in this study as a part of segmentation
stage to localize the iris accurately and remove unwanted area that might be included. The
obtained iris region is encoded using haar wavelets to construct the iris code, which contains
the most discriminating feature in the iris pattern. Hamming distance is used for comparison of
iris templates in the recognition stage. The dataset which is used for the study is UBIRIS
database. A comparative study of different edge detector operator is performed. It is observed
that canny operator is best suited to extract most of the edges to generate the iris code for
comparison. Recognition rate of 89% and rejection rate of 95% is achieved.
This document presents a new approach for human identification using sclera recognition. It begins with background on sclera and challenges with sclera recognition. It then describes the proposed methodology which includes sclera segmentation, feature extraction using Gabor filtering, and recognition using Bayesian classification. Experimental results show the false accept and reject rates for the approach. It concludes that sclera recognition is promising for human identification and can achieve accuracy comparable to iris recognition in visible light. The proposed approach uses Bayesian classification for recognition, which is more effective than previous matching score methods.
A Spectral Domain Local Feature Extraction Algorithm for Face RecognitionCSCJournals
In this paper, a spectral domain feature extraction algorithm for face recognition is proposed, which efficiently exploits the local spatial variations in a face image. For the purpose of feature extraction, instead of considering the entire face image, an entropy-based local band selection criterion is developed, which selects high-informative horizontal bands from the face image. In order to capture the local variations within these high-informative horizontal bands precisely, a feature selection algorithm based on two-dimensional discrete Fourier transform (2D-DFT) is proposed. Magnitudes corresponding to the dominant 2D-DFT coefficients are selected as features and shown to provide high within-class compactness and high between-class separability. A principal component analysis is performed to further reduce the dimensionality of the feature space. Extensive experimentations have been carried out upon standard face databases and the recognition performance is compared with some of the existing face recognition schemes. It is found that the proposed method offers not only computational savings but also a very high degree of recognition accuracy.
The document proposes a unified framework for iris recognition that addresses challenges in unconstrained acquisition, robust matching, and privacy. It uses random projections and sparse representations to select good quality iris images, recognize iris patterns in a single step, and introduce cancelable templates for enhanced privacy without compromising security or recognition performance. Experimental results on public datasets demonstrate benefits of the proposed approach for robust and accurate iris recognition.
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.
Human Segmentation Using Haar-ClassifierIJERA Editor
Segmentation is an important process in many aspects of multimedia applications. Fast and perfect segmentation of moving objects in video sequences is a basic task in many computer visions and video investigation applications. Particularly Human detection is an active research area in computer vision applications. Segmentation is very useful for tracking and recognition the object in a moving clip. The motion segmentation problem is studied and reviewed the most important techniques. We illustrate some common methods for segmenting the moving objects including background subtraction, temporal segmentation and edge detection. Contour and threshold are common methods for segmenting the objects in moving clip. These methods are widely exploited for moving object segmentation in many video surveillance applications, such as traffic monitoring, human motion capture. In this paper, Haar Classifier is used to detect humans in a moving video clip some features like face detection, eye detection, full body, upper body and lower body detection.
Constantine Kotropoulos, Associate Professor, Aristotle University of Thessaloniki, Department of Informatics, Sparse and Low Rank Representations in Music Signal Analysis
Nicholas Kalouptsidis, Professor, National and Kapodistrian University of Athens, Department of Informatics and Telecommunications, Nonlinear Communications: Achievable Rates, Estimation, and Decoding
Ahmed K. Elmagarmid (IEEE Fellow and ACM Distinguished Scientist) gave a lecture on Data Quality: Not Your Typical Database Problem in the Distinguished Lecturer Series - Leon The Mathematician.
This document summarizes a talk on influence propagation in large graphs. It discusses theorems and algorithms related to modeling the spread of information, viruses, and diseases over networks. The document begins by motivating the importance of understanding dynamical processes over networks through examples related to epidemiology, viral marketing, cybersecurity, and more. It then outlines threshold results for epidemic models on static graphs that depend on the largest eigenvalue of the graph's adjacency matrix and properties of the propagation model. The talk discusses proofs of these results and also covers extensions to dynamic graphs and competing viruses. Finally, it discusses algorithms for determining who to immunize to control outbreaks.
Professor Maria Petrou gave a lecture on "A Classification Framework for Software Component Models" in the Distinguished Lecturer Series - Leon The Mathematician.
More Information available at:
http://dls.csd.auth.gr
Professor Xin Yao gave a lecture on "Co-evolution, games, and social behaviors" in the Distinguished Lecturer Series - Leon The Mathematician.
More Information available at:
http://goo.gl/G7MdD
Professor Ismail Toroslu gave a lecture on "Web Usage Mining and Using Ontology for Capturing Web Usage Semantic" in the Distinguished Lecturer Series - Leon The Mathematician.
More Information available at:
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Professor Ivica Crnkovic gave a lecture on "A Classification Framework for Software Component Models" in the Distinguished Lecturer Series - Leon The Mathematician.
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This document discusses compressive spectral image sensing and optimization. It introduces compressive spectral imaging (CASSI) which uses coded apertures to sense a datacube with only N^2 measurements rather than the traditional N x N x L measurements. Coded apertures can be optimized for sensing and reconstruction performance as well as spectral selectivity and image classification. New families of coded apertures include boolean, spectrally selective, super-resolution, and colored apertures.
Professor Professor Joseph Sifakis gave a lecture on From Programs to Systems – Building a Smarter World in the Distinguished Lecturer Series - Leon The Mathematician.
Georgios Giannakis, Professor and ADC Chair in Wireless Telecommunications, University of Minnesota, Department of Electrical & Computer Engineering (IEEE/EURASIP Fellow, IEEE SPS DL), Sparsity Control for Robustness and Social Data Analysis
The document discusses the IEEE Signal Processing Society and the Greek signal processing community. It provides a brief history of signal processing and its influences from other fields. It notes the ubiquity of signals and signal processing. It then summarizes the current state and challenges facing the IEEE Signal Processing Society. It provides details on the local Greek SPS chapter, including its size, activities, and plans for coordinating with the broader Greek signal processing community. These plans include making the Greek SP Jam a regular event and establishing workshops, summer schools, lectures, decentralized events, and awards.
This document discusses using model checking techniques for safety critical systems at NASA. It begins by introducing model checking and how it can be used to verify that a program or model satisfies a given property. It then discusses challenges like the state explosion problem and presents compositional verification as a way to address this by breaking the verification task into checking smaller components. The document provides several examples of applying these techniques to real NASA systems like rovers and spacecraft software.
Professor Dr. Sudip Misra gave a lecture on "Jamming in Wireless Sensor Networks" in the Distinguished Lecturer Series - Leon The Mathematician.
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Aristidis Likas, Associate Professor and Christoforos Nikou, Assistant Professor, University of Ioannina, Department of Computer Science , Mixture Models for Image Analysis
Aggelos Katsaggelos, Professor and AT&T Chair, Northwestern University, Department of Electrical Engineering & Computer Science (IEEE/ SPIE Fellow, IEEE SPS DL), Sparse and Redundant Representations: Theory and Applications
Professor Michael Devetsikiotis gave a lecture on "Networked 3-D Virtual Collaboration in Science and Education: Towards 'Web 3.0' (A Modeling Perspective) " in the Distinguished Lecturer Series - Leon The Mathematician.
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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.
This document discusses machine learning tools and particle swarm optimization for content-based search in large multimedia databases. It begins with an outline and then covers topics like big data sources and characteristics, descriptive and prescriptive analytics using tools like particle swarm optimization, and methods for exploring big data including content-based image retrieval. It also discusses challenges like optimization of non-convex problems and proposes methods like multi-dimensional particle swarm optimization to address issues like premature convergence.
Face and Eye Detection Varying Scenarios With Haar Classifier_2015Showrav Mazumder
The document presents a face and eye detection system. It discusses challenges in face detection like image quality, pose variation, and facial expressions. It describes the history of face detection and various methods like knowledge-based, feature-invariant, template matching, and appearance-based. The methodology section explains the Viola-Jones algorithm using Haar-like features, integral images, AdaBoost, and cascade classifiers. The implementation uses OpenCV for detection. Experiments showed high detection rates for single faces but lower rates for group faces and detecting eyes with pose variations. Future work involves improving classifiers and detecting side faces in real-time.
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.
A Fast Recognition Method for Pose and Illumination Variant Faces on Video Se...IOSR Journals
This document summarizes a research paper that proposes a new face recognition method for video sequences with variations in pose and illumination. The proposed method uses an active appearance model without nonlinear programming to extract features, and a lazy classifier for recognition, in order to reduce computational complexity compared to previous methods. Experimental results show the proposed method achieves better recognition performance and lower computational cost than conventional techniques. The document provides background on video face recognition challenges and reviews related work on pose-invariant and illumination-robust recognition methods.
Multimodal Biometrics Recognition from Facial Video via Deep Learning cscpconf
Biometrics identification using multiple modalities has attracted the attention of many
researchers as it produces more robust and trustworthy results than single modality biometrics.
In this paper, we present a novel multimodal recognition system that trains a Deep Learning
Network to automatically learn features after extracting multiple biometric modalities from a
single data source, i.e., facial video clips. Utilizing different modalities, i.e., left ear, left profile
face, frontal face, right profile face, and right ear, present in the facial video clips, we train
supervised denosing autoencoders to automatically extract robust and non-redundant features.
The automatically learned features are then used to train modality specific sparse classifiers to
perform the multimodal recognition. Experiments conducted on the constrained facial video
dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a
99.17% and 97.14% rank-1 recognition rates, respectively. The multimodal recognition
accuracy demonstrates the superiority and robustness of the proposed approach irrespective of
the illumination, non-planar movement, and pose variations present in the video clips.
MULTIMODAL BIOMETRICS RECOGNITION FROM FACIAL VIDEO VIA DEEP LEARNINGcsandit
Biometrics identification using multiple modalities has attracted the attention of many researchers as it produces more robust and trustworthy results than single modality biometrics.
In this paper, we present a novel multimodal recognition system that trains a Deep Learning Network to automatically learn features after extracting multiple biometric modalities from a single data source, i.e., facial video clips. Utilizing different modalities, i.e., left ear, left profile face, frontal face, right profile face, and right ear, present in the facial video clips, we train supervised denosing autoencoders to automatically extract robust and non-redundant features.The automatically learned features are then used to train modality specific sparse classifiers to perform the multimodal recognition. Experiments conducted on the constrained facial video
dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a 99.17% and 97.14% rank-1 recognition rates, respectively. The multimodal recognition
accuracy demonstrates the superiority and robustness of the proposed approach irrespective of the illumination, non-planar movement, and pose variations present in the video clips.
Research and Development of DSP-Based Face Recognition System for Robotic Reh...IJCSES Journal
This article describes the development of DSP as the core of the face recognition system, on the basis of
understanding the background, significance and current research situation at home and abroad of face
recognition issue, having a in-depth study to face detection, Image preprocessing, feature extraction face
facial structure, facial expression feature extraction, classification and other issues during face recognition
and have achieved research and development of DSP-based face recognition system for robotic
rehabilitation nursing beds. The system uses a fixed-point DSP TMS320DM642 as a central processing
unit, with a strong processing performance, high flexibility and programmability.
The document summarizes and compares different methods for face recognition, including Eigenface, Line Edge Map (LEM), and other techniques. It provides descriptions of how each technique works, such as using eigenvectors to extract features for Eigenface. Experimental results show LEM achieves better accuracy than Eigenfaces for variations in lighting and size. While Eigenfaces struggles with size changes, LEM maintains high accuracy for different conditions. The document recommends future work combining techniques to maximize recognition accuracy.
IRJET- Human Face Recognition in Video using Convolutional Neural Network (CNN)IRJET Journal
This document describes a method for human face recognition in videos using convolutional neural networks (CNNs). It involves preprocessing video frames to grayscale, detecting faces using Viola-Jones detection, extracting features using BRISK, and classifying faces using a CNN. The CNN approach improves efficiency and accuracy in detecting faces under varying poses and illumination conditions compared to previous methods. The goal is to develop a system that can accurately recognize faces in videos despite challenges like illumination changes, poses, occlusions, etc.
A study of techniques for facial detection and expression classificationIJCSES Journal
Automatic recognition of facial expressions is an important component for human-machine interfaces. It
has lot of attraction in research area since 1990's.Although humans recognize face without effort or
delay, recognition by a machine is still a challenge. Some of its challenges are highly dynamic in their
orientation, lightening, scale, facial expression and occlusion. Applications are in the fields like user
authentication, person identification, video surveillance, information security, data privacy etc. The
various approaches for facial recognition are categorized into two namely holistic based facial
recognition and feature based facial recognition. Holistic based treat the image data as one entity without
isolating different region in the face where as feature based methods identify certain points on the face
such as eyes, nose and mouth etc. In this paper, facial expression recognition is analyzed with various
methods of facial detection,facial feature extraction and classification.
This document summarizes a research project on face and facial feature detection from images. The project uses the Viola-Jones object detection framework combined with geometric information to detect faces and locate features like eyes, nose, and mouth. Key steps include face detection using Haar-like features and AdaBoost classification, then detecting facial features based on characteristics like size, shape, color, and position relative to other facial features. MATLAB functions like videoinput and getsnapshot are used to acquire video frames and capture images for processing.
IRJET- Face Spoofing Detection Based on Texture Analysis and Color Space Conv...IRJET Journal
This document proposes a novel approach for face spoofing detection using color texture analysis. It extracts texture features from images converted to different color spaces like RGB, HSV and YCbCr. Key steps include face detection, normalization, color space conversion, texture feature extraction using methods like HOG, LBP and Gabor wavelets. Features are classified using an SVM classifier to detect live or spoofed faces. Experimental results on standard databases show the color texture representation provides stable performance across conditions compared to grayscale. The approach exploits complementary color texture information from luminance and chrominance channels to effectively detect face spoofing.
IRJET- A Survey on Facial Expression Recognition Robust to Partial OcclusionIRJET Journal
This document summarizes various approaches for facial expression recognition that are robust to partial facial occlusions. It begins by introducing the topic and importance of facial expression recognition systems that can handle real-world scenarios involving partial occlusions. It then categorizes and reviews key approaches in the literature, including feature reconstruction based on PCA or RPCA, sparse coding approaches using SRC or MLESR, sub-space based methods using Gabor filters or LGBPHS, and statistical prediction models using Bayesian or tracking methods. The document focuses on studies that have researched expression recognition for facial images with partial occlusions.
Optimization of Facial Landmark for Sentiment Analysis on Images with Human F...adewole63
Facial landmarks involve localizing key features on human faces, such as eyes, nose, and mouth. This technique is used in applications like video surveillance, computer vision, and human-computer interaction. However, facial landmark detection remains challenging due to issues including varying image quality, pose, lighting, and face shape/size. The document discusses different algorithms that can be used for facial landmark detection, including histogram of oriented gradients (HOG) and linear support vector machines (SVM). HOG counts gradient orientations in localized image regions and is robust to lighting changes, making it efficient for face detection when combined with machine and deep learning approaches.
This document presents a method for real-time facial expression analysis using principal component analysis (PCA). The method involves detecting faces, extracting expression features from the eye and mouth regions, applying PCA to extract texture features, and using a support vector machine classifier to classify expressions. The proposed approach was tested on a database of facial images with expressions categorized as happy, angry, disgust, sad, or neutral. PCA was used to select the most relevant eigenfaces and reduce the dimensionality of the feature space for more efficient classification of expressions in real-time.
Deep learning methods have improved machine capabilities for face recognition. Three key modules are typically used: 1) A face detector localizes faces in images using region-based or sliding window approaches with DCNNs. 2) A fiducial point detector localizes facial landmarks using DCNN regressors or 3D models. 3) A DCNN extracts features for face recognition and verification by computing similarity scores between representations. Large annotated datasets have enabled DCNNs to learn robust representations for unconstrained images.
Face recognition technology uses machine learning algorithms to identify or verify a person's identity from digital images or video frames. The process involves detecting faces, applying preprocessing techniques like filtering and scaling, training classifiers using labeled face images, and then classifying new faces. Common machine learning algorithms used include K-nearest neighbors, naive Bayes, decision trees, and locally weighted learning. The proposed system detects faces, builds a tabular dataset from pixel values, trains classifiers, and evaluates performance on a test set. Software applies techniques like detection, alignment, normalization, and matching to encode faces for comparison. Face recognition has advantages like convenience and low cost, and applications in security, banking, and more.
This document summarizes a student project on face recognition. It begins with an introduction to face recognition, its applications, and common challenges. It then reviews literature on existing face recognition methods and identifies problems related to tilted poses and variations in illumination and expression. The proposed method will work to improve recognition rates under these conditions in two phases - training and testing. The method aims to enhance the preprocessing and feature extraction steps to make the system more robust. A basic flowchart of the proposed approach is provided, and the document concludes with references.
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.
Biometrics refers to metrics related to human characteristics. Biometrics authentication (or realistic authentication) is used in computer science as a form of identification and access control. It is also used to identify individuals in groups that are under surveillance.
Similar to Semantic 3DTV Content Analysis and Description (20)
The document discusses efficient processing of complex data through BFS graph traversal. It describes constructing a graph from a data file by adding nodes and edges. It then performs BFS traversal on the graph, maintaining a queue of nodes to visit and propagating information to discover paths between nodes.
The document discusses artificial intelligence and human thinking. It proposes the Abductive Logic Programming (ALP) agent model as a unifying framework for both. ALP clausal logic can serve as the Language of Thought (LOT), representing a private, language-like representation in the mind. Additionally, ALP clausal logic can function as a connectionist model of the mind by representing concepts and relationships between concepts.
Associate Professor Anita Wasilewska gave a lecture on "Descriptive Granularity" in the Distinguished Lecturer Series - Leon The Mathematician.
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Professor Claes Wohlin gave a lecture on " Success Factors in Industry - Academia Collaboration - An Empirical Study " in the Distinguished Lecturer Series - Leon The Mathematician.
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Professor Gonzalo R. Arce gave a lecture on "Compressed sensing in spectral imaging" in the Distinguished Lecturer Series - Leon The Mathematician.
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More from Distinguished Lecturer Series - Leon The Mathematician (7)
Physiology and chemistry of skin and pigmentation, hairs, scalp, lips and nail, Cleansing cream, Lotions, Face powders, Face packs, Lipsticks, Bath products, soaps and baby product,
Preparation and standardization of the following : Tonic, Bleaches, Dentifrices and Mouth washes & Tooth Pastes, Cosmetics for Nails.
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
1. Human centered 2D/3D multiview
video analysis and description
Nikos Nikolaidis
Greek SP Jam, AUTH Ioannis Pitas*
17/5/2012 AIIA Lab
Aristotle University of Thessaloniki
Greece
2. Anthropocentric video content analysis
tasks
Face detection and tracking
Body or body parts (head/hand/torso) detection
Eye/mouth detection
3D face model reconstruction
Face clustering/recognition
Facial expression analysis
Activity/gesture recognition
Semantic video content description/annotation
3. Anthropocentric video content description
Applications in video content search (e.g. in YouTube)
Applications in film and games postproduction:
Semantic annotation of video
Video indexing and retrieval
Matting
3D reconstruction
Anthropocentric (human centered) approach
humans are the most important video entity.
6. Face detection and tracking
1st frame 6th frame 11th frame 16th frame
Problem statement:
To detect the human faces that appear in each video
frame and localize their Region-Of-Interest (ROI).
To track the detected faces over the video frames.
7. Face detection and tracking
Tracking associates each detected face in the current
video frame with one in the next video frame.
Therefore, we can describe the face (ROI) trajectory
in a shot in (x,y) coordinates.
Actor instance definition: face region of interest
(ROI) plus other info
Actor appearance definition: face trajectory plus
other info
10. Face detection and tracking
Face
Feature based face Select Features
Detection
tracking module
Track
Features
YES Occlusion
Handling
NO
Result
11. Face detection and tracking
Tracking failure may occur, i.e., when a face disappears.
In such cases, face re-detection is employed.
However, if any of the detected faces coincides with any of the
faces already being tracked, the former ones are kept, while the latter
ones are discarded from any further processing.
Periodic face re-detection can be applied to account for new faces
entering the camera's field-of-view (typically every 5 video frames).
Forward and backward tracking, when the entire video is
available.
17. Multiview human detection
Problem statement:
Use information from multiple cameras to detect
bodies or body parts, e.g. head.
Camera 4 Camera 6
Applications:
Human detection/localization in postproduction.
Matting / segmentation initialization.
18. Multiview human detection
The retained voxels are projected to all views.
For every view we reject ROIs that have small overlap
with the regions resulting from the projection.
Camera 2 Camera 4
19. Eye detection
Problem statement:
To detect eye regions in
an image.
Input: A facial ROI produced by face detection
or tracking
Output: eye ROIs / eye center location.
Applications:
face analysis e.g. expression recognition, face recognition,
etc
gaze tracking.
20. Eye detection
Eye detection based on edges and distance maps from
the eye and the surrounding area.
Robustness to illumination conditions variations.
The Canny edge detector is used.
For each pixel, a vector pointing to the closest edge
pixel is calculated. Its magnitude (length) and the slope
(angle) are used.
24. 3D face reconstruction from
uncalibrated video
Problem statement:
Input: facial images or facial video frames, taken from different view
angles, provided that the face neither changes expression nor speaks.
Output: 3D face model (saved as a VRML file) and its calibration in
relation to each camera. Facial pose estimation.
Applications:
3D face reconstruction, facial pose estimation, face recognition,
face verification.
25. 3D face reconstruction from
uncalibrated video
Method overview
Manual selection of characteristic feature points on the
input facial images.
Use of an uncalibrated 3-D reconstruction algorithm.
Incorporation of the CANDIDE generic face model.
Deformation of the generic face model based on the 3-D
reconstructed feature points.
Re-projection of the face model grid onto the images and
manual refinement.
26. 3D face reconstruction from
uncalibrated video
Input: three images with a number of matched
characteristic feature points.
27. 3D face reconstruction from
uncalibrated video
The CANDIDE face model has 104
nodes and 184 triangles.
Its nodes correspond to
characteristic points of the human
face, e.g. nose tip, outline of the
eyes, outline of the mouth etc.
29. 3D face reconstruction from uncalibrated video
Selected features
CANDIDE grid reprojection 3D face reconstruction
30. 3D face reconstruction from
uncalibrated video
Performance evaluation:
Reprojection error
31. Face clustering
Problem statement:
To cluster a set of facial ROIs
Input: a set of face image ROIs
Output: several face clusters, each containing faces of
only one person.
Applications
Cluster the actor images, even if they belong in
different shots.
Cluster varying views of the same actor.
32. Face clustering
A four step clustering algorithm is used:
face tracking;
calculation of mutual information between the tracked
facial ROIs to form a similarity matrix (graph);
application of face trajectory heuristics, to improve
clustering performance;
similarity graph clustering.
33. Face clustering
Performance evaluation
85.4% correct clustering rate, 6.6% clustering errors
An error of 7.9% is introduced by the face detector.
Face detector errors have been manually removed
93% correct clustering rate, 7% clustering errors
Cluster 2 results are improved
34. Face recognition
Problem statement:
To identify a face identity
Input: a facial ROI
Output: the face id
Hugh
Sandra Grant
Applications: Bullock
Finding the movie cast
Retrieving movies of an actor
Surveillance applications
35. Face recognition
Two general approaches:
Subspace methods
Elastic graph matching methods.
36. Face recognition
Subspace methods
The original high-dimensional image space is projected
onto a low-dimensional one.
Face recognition according to a simple distance measure in
the low dimensional space.
Subspace methods: Eigenfaces (PCA), Fisherfaces (LDA),
ICA, NMF.
Main limitation of subspace methods: they require perfect
face alignment (registration).
37. Face recognition
Elastic graph matching (EGM) methods
Elastic graph matching is a simplified implementation of the
Dynamic Link Architecture (DLA).
DLA represents an object by a rectangular elastic grid.
A Gabor wavelet bank response is measured at each grid node.
Multiscale dilation-erosion at each grid node can be used,
leading to Morphological EGM (MEGM).
38. Face recognition
Output of normalized multi-scale dilation-erosion for
nine scales.
39. Face recognition
Performance evaluation:
Performance metric: Equal Error Rate (EER).
M2VTS and XM2VTS facial image data base for
training/testing
MEGM EER: 2%
Subspace techniques EER 5-10%
Kernel subspace techniques: 2%
Elastic graph matching techniques have the best overall
performance.
40. Facial expression analysis
Problem statement:
To identify a facial expression
in a facial image or 3D point cloud.
Input: a face ROI or a point cloud
Output: the facial expression label
(e.g. neutral, anger, disgust, sadness, happiness, surprise, fear).
Applications
Affective video content description
41. Facial expression analysis
Two approaches for expression analysis on images /
videos:
Feature based ones
They use texture or geometrical information as features for
expression information extraction.
Template based ones
They use 3D or 2D head and facial models as templates for facial
expression recognition.
42. Facial expression analysis
Feature based methods
The features used can be:
geometric positions of a set of fiducial points on a
face;
multi-scale and multi-orientation Gabor wavelet
coefficients at the fiducial points.
Optical flow information
Transform (e.g. DCT, ICA, NMF) coefficients
Image pixels reduced in dimension by using PCA
or LDA.
45. 3D facial expression recognition
Experiments on the BU-3DFE database
100 subjects.
6 facial expressions x 4 different intensities +neutral per subject.
83 landmark points (used in our method).
Recognition rate: 93.6%
46. Action recognition
Problem statement:
To identify the action (elementary activity) of a person
Input: a single-view or multi-view video or a sequence of 3D
human body models (or point clouds).
Output: An activity label (walk, run, jump,…) for each frame or
for the entire sequence.
run walk jump p. jump f. bend
Applications:
Semantic video content description, indexing, retrieval
47. Action recognition
Action recognition on video data
Input feature vectors: binary masks resulting from coarse body
segmentation on each frame.
run walk jump p. jump f. bend
48. Action recognition
Perform fuzzy c-means (FCM) clustering on the feature
vectors of all frames of training action videos
49. Action recognition
Find the cluster centers, “dynemes”: key human
body poses
Characterize each action video by its distance
from the dynemes.
Apply Linear Discriminant Analysis (LDA) to
further decrease dimensionality.
50. Action recognition
Characterize each test video by its distances from the
dynemes
Perform LDA
Perform classification, e.g. by SVM
The method can operate on videos containing multiple
actions performed sequentially.
96% correct action recognition on a database containing 10
actions (walking, running, jumping, waving,..)
51. 3D action recognition
Action recognition on 3D data
Extension of the video-based activity recognition
algorithm
Input to the algorithm: binary voxel-based representation
of frames
52. Visual speech detection
Problem statement:
To detect video frames where
a persons speaks using only visual information
Input: A mouth ROI produced by mouth detection
Output: yes/no visual speech indication.
The mouth is detected and localized by a similar technique
to eye detection.
53. Visual speech detection
Applications
Detection of important video frames (when someone
speaks)
Lip reading applications
Speech recognition applications
Speech intent detection and speaker determination
human--computer interaction applications
video telephony and video conferencing systems.
Dialogue detection system for movies and TV programs
54. Visual speech detection
A statistical approach for visual speech detection,
using mouth region luminance will be presented.
The method employs face and mouth region
detectors, applying signal detection algorithms to
determine lip activity.
57. Visual speech detection
The number of low grayscale intensity pixels of the mouth
region in a video sequence. The rectangle encompasses the
frames where a person is speaking.
58. Anthropocentric video content description
structure based on MPEG-7
Problem statement:
To organize and store the extracted semantic information for
video content description.
Input: video analysis results
Output: MPEG-7 compatible XML file.
Applications
Video indexing and retrieval
Video metadata analysis
59. Anthropocentric video content description
Anthropos-7 metadata description profile
Humans and their status/actions are the most important
metadata items.
Anthropos-7 presents an anthropocentric Perspective for Movie
annotation.
New video content descriptors (on top of MPEG7 entities) are
introduced, in order to store in a better way intermediate and
high level video metadata related to humans.
Human ids, actions, status descriptions are supported.
Information like:
“Actor H. Grant appears in this shot and he is smiling”
can be described in the proposed Anthropos-7 profile.
60. Anthropocentric video content description
Main Anthropos-7 Characteristics:
Anthropos-7 Descriptors gather semantic (intermediate level)
video metadata (tags).
Tags are selected to suit research areas like face detection,
object tracking, motion detection, facial expression extraction
etc.
High level semantic video metadata (events, e.g. dialogues) are
supported.
61. Anthropocentric video content description
Ds & DSs Name
Movie Class
Version Class
Scene Class
Shot Class
Take Class
Frame Class
Sound Class
Actor Class
Actor Appearance Class
Object Appearance Class
Actor Instance Class
Object Instance Class
High Semantic Class
Camera Class
Camera Use Class
Lens Class
62. Anthropocentric video content description
Movie Class Actor Class
Version Class Object Appearance Class
Scene Class Actor Appearance Class
Shot Class Object Instance Class
Take Class Actor Instance Class
Frame Class High Semantic Class
Sound Class Camera Class
Camera Use Class
Lens Class
63. Anthropocentric video content description
Shot Class
ID
Movie Class Actor Class
Shot S/N
Version Class Object Appearance Class
Scene Class Actor Appearance Class Shot Description
Shot Class Object Instance Class Take Ref
Take Class Actor Instance Class
Frame Class High Semantic Class Start Time
Sound Class Camera Class End Time
Camera Use Class
Lens Class Duration
Camera Use
Key Frame
Number Of Frames
Color Information
Object Appearances
64. Anthropocentric video content description
Movie Class Actor Class
Version Class Object Appearance Class
Scene Class Actor Appearance Class
Shot Class Object Instance Class
Take Class Actor Instance Class Actor Class
Frame Class High Semantic Class
Sound Class Camera Class
ID
Camera Use Class
Lens Class
Name
Sex
Nationality
Role Name
65. Anthropocentric video content
description
Movie Class Actor Class
Version Class Object Appearance Class
Scene Class Actor Appearance Class Actor Appearance Class
Shot Class Object Instance Class
Take Class Actor Instance Class
ID
Frame Class High Semantic Class
Sound Class Camera Class
Camera Use Class Time In List
Lens Class
Time Out List
Color Information
Motion
Actor Instances
66. Anthropocentric video content description
Examples of Actor Instances in one Actor Appearence
1st frame 6th frame 11th frame 16th frame
67. Anthropocentric video content description
Actor Instance
Class
ID
ROIDescriptionModel
Movie Class Actor Class
Version Class Object Appearance Class ID
Scene Class Actor Appearance Class Geometry
Shot Class Object Instance Class
ID
Take Class Actor Instance Class
Frame Class High Semantic Class Feature Points
Sound Class Camera Class Bounding Box
Camera Use Class Convex Hull
Lens Class Grid
Color
Status
Position In Frame
Expressions
Activity
68. Anthropos-7 Video Annotation software
A set of video annotation software tools have been created for
anthropocentric video content description.
Anthropocentric Video Content Description Structure based on
MPEG-7.
These software tools perform (semi)-automatic video content
annotation.
The video metadata results (XML file) can be edited manually by a
software tool called Anthropos-7 editor.
69. Multi-view Anthropos-7
The goal is to link the frame-based information
instantiated by the ActorInstanceType DS at each
video channel (e.g. L+R channel in stereo videos)
In other words to link the instances of the
ActorInstanceType DS belonging in different
channels, due to disparity or color similarity
71. Multi-view Anthropos-7
The proposed structure organizes the data in 3 levels
Level 1
Refers to the Correspondences tag
Level 2
Refers to the CorrespondingInstancesType DS
Level 3
Refers to the CorrespondingInstanceType DS
74. Conclusions
Anthropocentric movie description is very important
because in most cases the movies are based on human
characters, their actions and status.
We presented a number of anthropocentric video
analysis and descrition techniques that have
applications in film/games postproduction as well as
in semantic video search
Similar descriptions can be devised for stereo and
multiview video.
75. 3DTVS project
European project: 3DTV Content Search (3DTVS)
Start: 1/11/2011
Duration: 3 years
www.3dtvs-project.eu
Stay tuned!
76. Thank you very much for your attention!
Further info:
www.aiia.csd.auth.gr
pitas@aiia.csd.auth.gr
Tel: +30-2310-996304