AI Presentation- Human Face Detection Techniques- By Sudeep KC
•Download as PPTX, PDF•
0 likes•21 views
This document summarizes and compares different techniques for human face detection. It covers two main approaches: feature-based approaches which analyze facial geometry and components, and image-based approaches which use techniques like neural networks, linear subspace methods, and statistical approaches to detect faces in images. The document also discusses challenges like illumination changes and expressions. It concludes that neural networks are among the most efficient current algorithms for face detection, and that feature-based approaches work best for real-time detection while image-based approaches perform well on grayscale images. Future research directions discussed include detecting faces with masks and fusing multiple algorithms.
Report
Share
Report
Share
Recommended
Innovative Analytic and Holistic Combined Face Recognition and Verification M...
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%.
Age and Gender Classification using Convolutional Neural Network
This document describes a study that aims to accurately identify the gender and age range of facial images using convolutional neural networks. It begins with an introduction to age and gender classification and some of the challenges. It then discusses the system analysis and design, including the use of convolutional neural networks and machine learning techniques. The implementation section notes that Python, TensorFlow, Keras and OpenCV will be used to build a convolutional neural network model to detect faces in images and predict age and gender through training on available datasets. The overall goal is to develop an accurate system for age and gender detection from facial images.
IRJET- A Comprehensive Survey and Detailed Study on Various Face Recognition ...
This document provides a comprehensive survey and detailed study of various face recognition methods. It begins with an introduction to face recognition and its advantages over other biometric methods. It then categorizes face recognition methods into four types: knowledge-based, feature-based, template matching, and appearance-based. The majority of the document discusses these methods in further detail and provides examples of algorithms that fall under each type, such as Eigenfaces, LDA, ICA, and neural networks. It concludes by stating that reviewing existing face recognition algorithms may lead to improved methods for solving this fundamental problem.
184
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.
Kh3418561861
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.
ACET.pptx
This document provides an overview of facial recognition technology and its applications. It discusses how facial recognition systems work by using nodal points on faces to recognize individuals. The objectives are to design software that can accurately detect faces from images without physical interaction and allow for high identification and verification rates. The research methodology involves a workflow for the facial recognition system. Potential applications mentioned include using facial recognition for computer and border security, voting verification, and commercial uses like residential security and banking.
HUMAN FACE RECOGNITION USING IMAGE PROCESSING PCA AND NEURAL NETWORK
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.
Review of face detection systems based artificial neural networks algorithms
This document provides a review of face detection systems that are based on artificial neural network algorithms. It summarizes several studies that have used different types of neural networks for face detection, including:
1) Retinal connected neural networks and rotation invariant neural networks.
2) Principal component analysis combined with neural networks.
3) Convolutional neural networks, multilayer perceptrons, backpropagation neural networks, and polynomial neural networks.
4) Fast neural networks, evolutionary optimization of neural networks, and Gabor wavelet features with neural networks. Strengths and limitations of these different approaches are discussed.
Recommended
Innovative Analytic and Holistic Combined Face Recognition and Verification M...
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%.
Age and Gender Classification using Convolutional Neural Network
This document describes a study that aims to accurately identify the gender and age range of facial images using convolutional neural networks. It begins with an introduction to age and gender classification and some of the challenges. It then discusses the system analysis and design, including the use of convolutional neural networks and machine learning techniques. The implementation section notes that Python, TensorFlow, Keras and OpenCV will be used to build a convolutional neural network model to detect faces in images and predict age and gender through training on available datasets. The overall goal is to develop an accurate system for age and gender detection from facial images.
IRJET- A Comprehensive Survey and Detailed Study on Various Face Recognition ...
This document provides a comprehensive survey and detailed study of various face recognition methods. It begins with an introduction to face recognition and its advantages over other biometric methods. It then categorizes face recognition methods into four types: knowledge-based, feature-based, template matching, and appearance-based. The majority of the document discusses these methods in further detail and provides examples of algorithms that fall under each type, such as Eigenfaces, LDA, ICA, and neural networks. It concludes by stating that reviewing existing face recognition algorithms may lead to improved methods for solving this fundamental problem.
184
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.
Kh3418561861
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.
ACET.pptx
This document provides an overview of facial recognition technology and its applications. It discusses how facial recognition systems work by using nodal points on faces to recognize individuals. The objectives are to design software that can accurately detect faces from images without physical interaction and allow for high identification and verification rates. The research methodology involves a workflow for the facial recognition system. Potential applications mentioned include using facial recognition for computer and border security, voting verification, and commercial uses like residential security and banking.
HUMAN FACE RECOGNITION USING IMAGE PROCESSING PCA AND NEURAL NETWORK
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.
Review of face detection systems based artificial neural networks algorithms
This document provides a review of face detection systems that are based on artificial neural network algorithms. It summarizes several studies that have used different types of neural networks for face detection, including:
1) Retinal connected neural networks and rotation invariant neural networks.
2) Principal component analysis combined with neural networks.
3) Convolutional neural networks, multilayer perceptrons, backpropagation neural networks, and polynomial neural networks.
4) Fast neural networks, evolutionary optimization of neural networks, and Gabor wavelet features with neural networks. Strengths and limitations of these different approaches are discussed.
REVIEW OF FACE DETECTION SYSTEMS BASED ARTIFICIAL NEURAL NETWORKS ALGORITHMS
Face detection is one of the most relevant applications of image processing and biometric systems.
Artificial neural networks (ANN) have been used in the field of image processing and pattern recognition.
There is lack of literature surveys which give overview about the studies and researches related to the using
of ANN in face detection. Therefore, this research includes a general review of face detection studies and
systems which based on different ANN approaches and algorithms. The strengths and limitations of these
literature studies and systems were included also.
Face recognition system
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.
Dissertation final report
This document is a dissertation submitted by Smriti Tikoo for the fulfillment of requirements for a Master's degree in Electronics and Communication Engineering. The dissertation focuses on facial detection using the Viola-Jones algorithm and facial recognition using a Backpropagation Neural Network. The document begins with an introduction that discusses the history and importance of facial recognition. It then covers topics like facial detection techniques, neural networks, and the proposed methodology which involves Viola-Jones for detection and a Backpropagation Neural Network for recognition. The document is organized into chapters that discuss the literature review, proposed methodology, software implementation, results and discussion, and conclusions.
Paper id 24201475
This document summarizes a research paper on face recognition using principal component analysis (PCA). It discusses how PCA can be used to reduce the dimensionality of face images for recognition. The system detects faces in images, extracts features using PCA, and then compares new faces to those in a training database to recognize identities. The results showed an accuracy of 87.09% on a test set of 30 images using this PCA-based approach for face recognition. While effective, the system has limitations when faces vary significantly from the training data. Overall, PCA provides a way to analyze face patterns and identify faces with reasonable accuracy under controlled conditions.
Face Verification Across Age Progression using Enhanced Convolution Neural Ne...
This paper proposes a deep learning method for facial verification of aging subjects. Facial aging is a
texture and shape variations that affect the human face as time progresses. Accordingly, there is a demand
to develop robust methods to verify facial images when they age. In this paper, a deep learning method
based on GoogLeNet pre-trained convolution network fused with Histogram Orientation Gradient (HOG)
and Local Binary Pattern (LBP) feature descriptors have been applied for feature extraction and
classification
FACE VERIFICATION ACROSS AGE PROGRESSION USING ENHANCED CONVOLUTION NEURAL NE...
This paper proposes a deep learning method for facial verification of aging subjects. Facial aging is a texture and shape variations that affect the human face as time progresses. Accordingly, there is a demand to develop robust methods to verify facial images when they age. In this paper, a deep learning method based on GoogLeNet pre-trained convolution network fused with Histogram Orientation Gradient (HOG) and Local Binary Pattern (LBP) feature descriptors have been applied for feature extraction and classification. The experiments are based on the facial images collected from MORPH and FG-Net benchmarked datasets. Euclidean distance has been used to measure the similarity between pairs of feature vectors with the age gap. Experiments results show an improvement in the validation accuracy conducted on the FG-NET database, which it reached 100%, while with MORPH database the validation accuracy is 99.8%. The proposed method has better performance and higher accuracy than current state-of-the-art methods.
IRJET - Real Time Facial Analysis using Tensorflowand OpenCV
This document presents a real-time facial analysis system using TensorFlow and OpenCV. The system can detect facial expressions, age, and gender from images and video in real-time. It uses deep learning models trained on facial datasets to analyze faces. The system is designed for applications like security, attendance tracking, and finding lost children. It works by extracting facial features from images, applying preprocessing techniques, classifying faces, and making predictions about attributes. The document discusses the methodology, existing techniques like PCA and HMM, the proposed system architecture, sample code, and conclusions.
Facial recognition system
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.
Human Face Detection and Tracking for Age Rank, Weight and Gender Estimation ...
This paper describes the technique for real time human face detection and tracking for age rank, weight and gender estimation. Face detection is involved with finding whether there are any faces in a given image and if there are any faces present, track the face and returns the face region with features of each face. Here it describes a simple and convenient hardware implementation of face detection method using Raspberry Pi Processor, which itself is a minicomputer of a credit card size. This paper presents a cost-sensitive ordinal hyperplanes ranking algorithm for human age evaluation based on face images. Two main components for building an efficient age estimator are facial feature extraction and estimator learning. Using feature extraction and comparing with our input database in which we have different age group face images with weight is specified according to that we also specify weight category i.e. under weight, normal weight and overweight . In this article we present gender estimation technique, which effectively integrates the head as well as mouth motion information with facial appearance by taking advantage of a unified probabilistic framework. Facial appearance as well as head and mouth motion possess a potentially relevant discriminatory power, and that the integration of different sources of biometric data from video sequences is the key approach to develop more precise and reliable realization systems.
Comparative Analysis of Face Recognition Methodologies and Techniques
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.
AN IMPROVED TECHNIQUE FOR HUMAN FACE RECOGNITION USING IMAGE PROCESSING
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.
Deep learning on face recognition (use case, development and risk)
1) Face recognition using deep learning methods has achieved high accuracy, nearing and sometimes surpassing human-level performance on some datasets.
2) The document outlines the key steps in face recognition systems using deep learning: face detection, alignment, feature extraction, and recognition. It discusses several influential deep learning models that have improved accuracy.
3) Applications discussed include security, health, and marketing/retail uses. Concerns about bias and privacy are also mentioned.
Detection and recognition of face using neural network
This document describes research on face detection and recognition using neural networks. It discusses using the Viola-Jones algorithm for face detection and a backpropagation neural network for face recognition. The Viola-Jones algorithm uses haar features, integral images, AdaBoost training, and cascading classifiers for real-time face detection. A backpropagation network with sigmoid activation functions is trained on facial images for recognition. Results show the network can accurately recognize faces after training. The document concludes the approach allows face recognition from an input image and discusses limitations and potential improvements.
Facial Emotion Recognition: A Survey
This document provides an overview of facial emotion recognition techniques. It discusses face detection methods like Viola-Jones, multi-task cascaded convolutional neural networks (CNN), and R-CNN. For face recognition, it covers linear approaches like Eigenfaces, Fisherfaces, and Marginfaces. It also discusses CNN and HOG methods for emotion detection, like AlexNet CNN and HOG-ESRs. Popular datasets for evaluation include CK+, JAFFE, and FER2013. The document surveys key algorithms and approaches for face detection, recognition, and emotion detection.
IRJET - Emotionalizer : Face Emotion Detection System
This document describes a facial emotion detection system called Emotionalizer. The system uses machine learning to analyze facial expressions in images and detect emotions like happy, sad, angry, fearful and disgust. It was developed in Python using techniques like pre-processing, skin color detection, facial feature extraction and a support vector machine classifier. The goal is to build a system that can automatically recognize emotions from faces as accurately as humans. It discusses previous related work on facial recognition and detection and outlines the objectives, methodology and evaluation of the Emotionalizer system.
IRJET- Emotionalizer : Face Emotion Detection System
This document describes a face emotion detection system called Emotionalizer. It uses machine learning and facial recognition techniques to detect emotions like happy, sad, angry, fearful and disgust based on facial expressions. The system analyzes images of faces and determines the appropriate emotion based on geometric changes in facial features. It was developed in Python using tools like OpenCV for facial detection and recognition. The goal is to build a system that can read emotions from facial expressions similarly to how humans perceive emotions.
Face Detection Using MATLAB (SUD)
This Presentation is for project work which will work on the "FACE DETECTION USING MATLAB".
This presentation will be prepared on the practical basis instead of theoretical knowledge. So result may vary on the basis of your practical work.
This Presentation is of standard format which is also beneficial for the engineering student for project work.
Person identification based on facial biometrics in different lighting condit...
Technological development is an inherent feature of this time, that reliance on electronic applications in all daily transactions (business management, banking, financial transfers, health, and other important aspects of life). Identifying and confirming identity is one of the complex challenges. Therefore, relying on biological properties gives reliable results. People can be identified in pictures, films, or real-time using facial recognition technology. A face individual is a unique identifying biological characteristic to authenticate them and prevents permits another person to assume that individual’s identity without their knowledge or consent. This article proposes the identification model by facial individual characteristics, based on the deep neural network (DNN). The proposed method extracts the spatial information available in an image, analysis this information to extract the salient features, and makes the identifying decision based on these features. This model presents successful and promising results, the accuracy achieves by the proposed system reaches 99.5% (+/- 0.16%) and the values of the loss function reach 0.0308 over the Pins Face Recognition dataset to identify 105 subjects.
Cross Pose Facial Recognition Method for Tracking any Person's Location an Ap...
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
Movie on face recognition in e attendace
This case study examines face recognition technology for e-attendance. It discusses the history of face recognition, defines biometrics, and explains why face recognition was chosen over other biometrics. The key components of face recognition systems are described, including how the systems work by detecting, aligning, normalizing, and matching facial features. Current and potential future applications of the technology are also outlined.
More Related Content
Similar to AI Presentation- Human Face Detection Techniques- By Sudeep KC
REVIEW OF FACE DETECTION SYSTEMS BASED ARTIFICIAL NEURAL NETWORKS ALGORITHMS
Face detection is one of the most relevant applications of image processing and biometric systems.
Artificial neural networks (ANN) have been used in the field of image processing and pattern recognition.
There is lack of literature surveys which give overview about the studies and researches related to the using
of ANN in face detection. Therefore, this research includes a general review of face detection studies and
systems which based on different ANN approaches and algorithms. The strengths and limitations of these
literature studies and systems were included also.
Face recognition system
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.
Dissertation final report
This document is a dissertation submitted by Smriti Tikoo for the fulfillment of requirements for a Master's degree in Electronics and Communication Engineering. The dissertation focuses on facial detection using the Viola-Jones algorithm and facial recognition using a Backpropagation Neural Network. The document begins with an introduction that discusses the history and importance of facial recognition. It then covers topics like facial detection techniques, neural networks, and the proposed methodology which involves Viola-Jones for detection and a Backpropagation Neural Network for recognition. The document is organized into chapters that discuss the literature review, proposed methodology, software implementation, results and discussion, and conclusions.
Paper id 24201475
This document summarizes a research paper on face recognition using principal component analysis (PCA). It discusses how PCA can be used to reduce the dimensionality of face images for recognition. The system detects faces in images, extracts features using PCA, and then compares new faces to those in a training database to recognize identities. The results showed an accuracy of 87.09% on a test set of 30 images using this PCA-based approach for face recognition. While effective, the system has limitations when faces vary significantly from the training data. Overall, PCA provides a way to analyze face patterns and identify faces with reasonable accuracy under controlled conditions.
Face Verification Across Age Progression using Enhanced Convolution Neural Ne...
This paper proposes a deep learning method for facial verification of aging subjects. Facial aging is a
texture and shape variations that affect the human face as time progresses. Accordingly, there is a demand
to develop robust methods to verify facial images when they age. In this paper, a deep learning method
based on GoogLeNet pre-trained convolution network fused with Histogram Orientation Gradient (HOG)
and Local Binary Pattern (LBP) feature descriptors have been applied for feature extraction and
classification
FACE VERIFICATION ACROSS AGE PROGRESSION USING ENHANCED CONVOLUTION NEURAL NE...
This paper proposes a deep learning method for facial verification of aging subjects. Facial aging is a texture and shape variations that affect the human face as time progresses. Accordingly, there is a demand to develop robust methods to verify facial images when they age. In this paper, a deep learning method based on GoogLeNet pre-trained convolution network fused with Histogram Orientation Gradient (HOG) and Local Binary Pattern (LBP) feature descriptors have been applied for feature extraction and classification. The experiments are based on the facial images collected from MORPH and FG-Net benchmarked datasets. Euclidean distance has been used to measure the similarity between pairs of feature vectors with the age gap. Experiments results show an improvement in the validation accuracy conducted on the FG-NET database, which it reached 100%, while with MORPH database the validation accuracy is 99.8%. The proposed method has better performance and higher accuracy than current state-of-the-art methods.
IRJET - Real Time Facial Analysis using Tensorflowand OpenCV
This document presents a real-time facial analysis system using TensorFlow and OpenCV. The system can detect facial expressions, age, and gender from images and video in real-time. It uses deep learning models trained on facial datasets to analyze faces. The system is designed for applications like security, attendance tracking, and finding lost children. It works by extracting facial features from images, applying preprocessing techniques, classifying faces, and making predictions about attributes. The document discusses the methodology, existing techniques like PCA and HMM, the proposed system architecture, sample code, and conclusions.
Facial recognition system
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.
Human Face Detection and Tracking for Age Rank, Weight and Gender Estimation ...
This paper describes the technique for real time human face detection and tracking for age rank, weight and gender estimation. Face detection is involved with finding whether there are any faces in a given image and if there are any faces present, track the face and returns the face region with features of each face. Here it describes a simple and convenient hardware implementation of face detection method using Raspberry Pi Processor, which itself is a minicomputer of a credit card size. This paper presents a cost-sensitive ordinal hyperplanes ranking algorithm for human age evaluation based on face images. Two main components for building an efficient age estimator are facial feature extraction and estimator learning. Using feature extraction and comparing with our input database in which we have different age group face images with weight is specified according to that we also specify weight category i.e. under weight, normal weight and overweight . In this article we present gender estimation technique, which effectively integrates the head as well as mouth motion information with facial appearance by taking advantage of a unified probabilistic framework. Facial appearance as well as head and mouth motion possess a potentially relevant discriminatory power, and that the integration of different sources of biometric data from video sequences is the key approach to develop more precise and reliable realization systems.
Comparative Analysis of Face Recognition Methodologies and Techniques
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.
AN IMPROVED TECHNIQUE FOR HUMAN FACE RECOGNITION USING IMAGE PROCESSING
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.
Deep learning on face recognition (use case, development and risk)
1) Face recognition using deep learning methods has achieved high accuracy, nearing and sometimes surpassing human-level performance on some datasets.
2) The document outlines the key steps in face recognition systems using deep learning: face detection, alignment, feature extraction, and recognition. It discusses several influential deep learning models that have improved accuracy.
3) Applications discussed include security, health, and marketing/retail uses. Concerns about bias and privacy are also mentioned.
Detection and recognition of face using neural network
This document describes research on face detection and recognition using neural networks. It discusses using the Viola-Jones algorithm for face detection and a backpropagation neural network for face recognition. The Viola-Jones algorithm uses haar features, integral images, AdaBoost training, and cascading classifiers for real-time face detection. A backpropagation network with sigmoid activation functions is trained on facial images for recognition. Results show the network can accurately recognize faces after training. The document concludes the approach allows face recognition from an input image and discusses limitations and potential improvements.
Facial Emotion Recognition: A Survey
This document provides an overview of facial emotion recognition techniques. It discusses face detection methods like Viola-Jones, multi-task cascaded convolutional neural networks (CNN), and R-CNN. For face recognition, it covers linear approaches like Eigenfaces, Fisherfaces, and Marginfaces. It also discusses CNN and HOG methods for emotion detection, like AlexNet CNN and HOG-ESRs. Popular datasets for evaluation include CK+, JAFFE, and FER2013. The document surveys key algorithms and approaches for face detection, recognition, and emotion detection.
IRJET - Emotionalizer : Face Emotion Detection System
This document describes a facial emotion detection system called Emotionalizer. The system uses machine learning to analyze facial expressions in images and detect emotions like happy, sad, angry, fearful and disgust. It was developed in Python using techniques like pre-processing, skin color detection, facial feature extraction and a support vector machine classifier. The goal is to build a system that can automatically recognize emotions from faces as accurately as humans. It discusses previous related work on facial recognition and detection and outlines the objectives, methodology and evaluation of the Emotionalizer system.
IRJET- Emotionalizer : Face Emotion Detection System
This document describes a face emotion detection system called Emotionalizer. It uses machine learning and facial recognition techniques to detect emotions like happy, sad, angry, fearful and disgust based on facial expressions. The system analyzes images of faces and determines the appropriate emotion based on geometric changes in facial features. It was developed in Python using tools like OpenCV for facial detection and recognition. The goal is to build a system that can read emotions from facial expressions similarly to how humans perceive emotions.
Face Detection Using MATLAB (SUD)
This Presentation is for project work which will work on the "FACE DETECTION USING MATLAB".
This presentation will be prepared on the practical basis instead of theoretical knowledge. So result may vary on the basis of your practical work.
This Presentation is of standard format which is also beneficial for the engineering student for project work.
Person identification based on facial biometrics in different lighting condit...
Technological development is an inherent feature of this time, that reliance on electronic applications in all daily transactions (business management, banking, financial transfers, health, and other important aspects of life). Identifying and confirming identity is one of the complex challenges. Therefore, relying on biological properties gives reliable results. People can be identified in pictures, films, or real-time using facial recognition technology. A face individual is a unique identifying biological characteristic to authenticate them and prevents permits another person to assume that individual’s identity without their knowledge or consent. This article proposes the identification model by facial individual characteristics, based on the deep neural network (DNN). The proposed method extracts the spatial information available in an image, analysis this information to extract the salient features, and makes the identifying decision based on these features. This model presents successful and promising results, the accuracy achieves by the proposed system reaches 99.5% (+/- 0.16%) and the values of the loss function reach 0.0308 over the Pins Face Recognition dataset to identify 105 subjects.
Cross Pose Facial Recognition Method for Tracking any Person's Location an Ap...
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
Movie on face recognition in e attendace
This case study examines face recognition technology for e-attendance. It discusses the history of face recognition, defines biometrics, and explains why face recognition was chosen over other biometrics. The key components of face recognition systems are described, including how the systems work by detecting, aligning, normalizing, and matching facial features. Current and potential future applications of the technology are also outlined.
Similar to AI Presentation- Human Face Detection Techniques- By Sudeep KC (20)
REVIEW OF FACE DETECTION SYSTEMS BASED ARTIFICIAL NEURAL NETWORKS ALGORITHMS
REVIEW OF FACE DETECTION SYSTEMS BASED ARTIFICIAL NEURAL NETWORKS ALGORITHMS
Face Verification Across Age Progression using Enhanced Convolution Neural Ne...
Face Verification Across Age Progression using Enhanced Convolution Neural Ne...
FACE VERIFICATION ACROSS AGE PROGRESSION USING ENHANCED CONVOLUTION NEURAL NE...
FACE VERIFICATION ACROSS AGE PROGRESSION USING ENHANCED CONVOLUTION NEURAL NE...
IRJET - Real Time Facial Analysis using Tensorflowand OpenCV
IRJET - Real Time Facial Analysis using Tensorflowand OpenCV
Human Face Detection and Tracking for Age Rank, Weight and Gender Estimation ...
Human Face Detection and Tracking for Age Rank, Weight and Gender Estimation ...
Comparative Analysis of Face Recognition Methodologies and Techniques
Comparative Analysis of Face Recognition Methodologies and Techniques
AN IMPROVED TECHNIQUE FOR HUMAN FACE RECOGNITION USING IMAGE PROCESSING
AN IMPROVED TECHNIQUE FOR HUMAN FACE RECOGNITION USING IMAGE PROCESSING
Deep learning on face recognition (use case, development and risk)
Deep learning on face recognition (use case, development and risk)
Detection and recognition of face using neural network
Detection and recognition of face using neural network
IRJET - Emotionalizer : Face Emotion Detection System
IRJET - Emotionalizer : Face Emotion Detection System
IRJET- Emotionalizer : Face Emotion Detection System
IRJET- Emotionalizer : Face Emotion Detection System
Person identification based on facial biometrics in different lighting condit...
Person identification based on facial biometrics in different lighting condit...
Cross Pose Facial Recognition Method for Tracking any Person's Location an Ap...
Cross Pose Facial Recognition Method for Tracking any Person's Location an Ap...
Recently uploaded
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...Nguyen Thanh Tu Collection
https://app.box.com/s/tacvl9ekroe9hqupdnjruiypvm9rdaneAccounting for Restricted Grants When and How To Record Properly
In this webinar, member learned how to stay in compliance with generally accepted accounting principles (GAAP) for restricted grants.
A Free 200-Page eBook ~ Brain and Mind Exercise.pptx
(A Free eBook comprising 3 Sets of Presentation of a selection of Puzzles, Brain Teasers and Thinking Problems to exercise both the mind and the Right and Left Brain. To help keep the mind and brain fit and healthy. Good for both the young and old alike.
Answers are given for all the puzzles and problems.)
With Metta,
Bro. Oh Teik Bin 🙏🤓🤔🥰
How Barcodes Can Be Leveraged Within Odoo 17
In this presentation, we will explore how barcodes can be leveraged within Odoo 17 to streamline our manufacturing processes. We will cover the configuration steps, how to utilize barcodes in different manufacturing scenarios, and the overall benefits of implementing this technology.
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...
Andreas Schleicher, Director of Education and Skills at the OECD presents at the launch of PISA 2022 Volume III - Creative Minds, Creative Schools on 18 June 2024.
Temple of Asclepius in Thrace. Excavation results
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...
The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.
Jemison, MacLaughlin, and Majumder "Broadening Pathways for Editors and Authors"
Jemison, MacLaughlin, and Majumder "Broadening Pathways for Editors and Authors"National Information Standards Organization (NISO)
This presentation was provided by Racquel Jemison, Ph.D., Christina MacLaughlin, Ph.D., and Paulomi Majumder. Ph.D., all of the American Chemical Society, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.BIOLOGY NATIONAL EXAMINATION COUNCIL (NECO) 2024 PRACTICAL MANUAL.pptx
Practical manual for National Examination Council, Nigeria.
Contains guides on answering questions on the specimens provided
How to Download & Install Module From the Odoo App Store in Odoo 17
Custom modules offer the flexibility to extend Odoo's capabilities, address unique requirements, and optimize workflows to align seamlessly with your organization's processes. By leveraging custom modules, businesses can unlock greater efficiency, productivity, and innovation, empowering them to stay competitive in today's dynamic market landscape. In this tutorial, we'll guide you step by step on how to easily download and install modules from the Odoo App Store.
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.ppt
The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
Confiscations, Kotahitanga, Kingitanga, Parliament, Suffrage, Repudiation, Economic Change, Agriculture, Gold Mining, Timber, Flax, Sheep, Dairying,
Recently uploaded (20)
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...
BÀI TẬP BỔ TRỢ TIẾNG ANH LỚP 9 CẢ NĂM - GLOBAL SUCCESS - NĂM HỌC 2024-2025 - ...
Accounting for Restricted Grants When and How To Record Properly
Accounting for Restricted Grants When and How To Record Properly
REASIGNACION 2024 UGEL CHUPACA 2024 UGEL CHUPACA.pdf
REASIGNACION 2024 UGEL CHUPACA 2024 UGEL CHUPACA.pdf
A Free 200-Page eBook ~ Brain and Mind Exercise.pptx
A Free 200-Page eBook ~ Brain and Mind Exercise.pptx
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...
Jemison, MacLaughlin, and Majumder "Broadening Pathways for Editors and Authors"
Jemison, MacLaughlin, and Majumder "Broadening Pathways for Editors and Authors"
BIOLOGY NATIONAL EXAMINATION COUNCIL (NECO) 2024 PRACTICAL MANUAL.pptx
BIOLOGY NATIONAL EXAMINATION COUNCIL (NECO) 2024 PRACTICAL MANUAL.pptx
Juneteenth Freedom Day 2024 David Douglas School District
Juneteenth Freedom Day 2024 David Douglas School District
How to Download & Install Module From the Odoo App Store in Odoo 17
How to Download & Install Module From the Odoo App Store in Odoo 17
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.ppt
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.ppt
AI Presentation- Human Face Detection Techniques- By Sudeep KC
- 1. Human Face Detection Techniques: A Comprehensive Review and Future Research Directions Course Code: MDS 556 Presenter: Sudeep K C Under the Supervision of Jagdish Bhatta
- 2. Abstract In modern times, face detection has become one of the key aspects of computer vision. There are at least two reasons for this trend; the first is the commercial and law enforcement applications, and the second is the availability of feasible technologies after years of research. This paper describes the different algorithms of facial detection and compared their recognition accuracies along with their working procedure, strength and limitation. Keywords: face detection; neural networks; feature-based approaches; image-based approaches; statistical approaches
- 3. Problem Statement Previous generations of face detection algorithms differ in accuracy with different factor as races or other data-driven and scenario. Also the dataset difficulty impact the accuracy a lot.
- 4. Introduction The face detection problem involves finding faces in still images and video frame using computer vision. As well as being the first step for many face-related technologies, this technology can also be used to verify faces, model faces, track head poses, recognize gender, age, and facial expressions, among others.
- 5. Different issues for face detection (illumination tolerance, facial expressions variations, and pose variations) has been presented
- 6. The research paper covers two approaches: • Feature Based Approach - Concerning with segmentation ie, component of face, facial geometry ie, link with facial features. Different method where used during this approach:
- 9. Comparison Performance Better Low Level Analysis Feature Analysis Good Active Shape Model DeformableTemplate Matching Edge Motion Feature Searching Deformable Part Model Point Distribution Model Color Motion Constellation Analysis
- 10. Image Based Approach • Most image-based approaches start by detecting faces on cluttered backgrounds. • A window-based scanning method is used in most image-based face detection techniques that is pixel to pixel scanning to search face and non face object. • Classified into three major fields: neural networks, linear subspace methods and statistical approaches
- 11. Process of Detecting Face in ANN
- 12. PCA
- 13. Performance Good Linear Subsequence Fast Statistical Approach High Performance Neural Network Eigenface PCA ANN Tensorface SVM DBNN Fisherface ICA FNN
- 14. Future Research • Face Mask & Face Shield • Fusion of Algorithm
- 15. Conclusions • A comprehensive survey of face detection methods is presented in this paper, with an emphasis on feature-based and image-based methods. • In the face detection field, NNs are among the most efficient algorithms and the most recent. Real-time detection is highly applicable to feature-based approaches, while gray-scale images are well suited to image-based approaches. • Apart from face detection, the algorithms reviewed were also extensively used for fault diagnosis, the analysis of EEG data, and the recognition of different patterns. • A good way to choose any algorithm is to know the problem to be solved and the algorithm that is best suited for solving the problem. • False positives will become increasingly problematic in more critical applications, such as payment verification, security, healthcare, criminal identification, and fake identity detection, Dark side of AI.
- 16. Refrences • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7879975/ • https://arxiv.org/ftp/arxiv/papers/1210/1210.1916.pdf • A Comparative Study on Facial Recognition Algorithms Sanmoy Paul and Sameer Acharya, NMIMS University, Mumbai • https://www.un.org/counterterrorism/sites/www.un.org.counterterrorism/files/malicious-use-of-ai-uncct-unicri-report- hd.pdf • https://personalpages.manchester.ac.uk/staff/timothy.f.cootes/Papers/asm_overview.pdf • https://www.hindawi.com/journals/aans/2011/673016/ • https://www.researchgate.net/publication/235712478_Image-based_Face_Detection_and_Recognition_State_of_the_Art • https://www.face-rec.org/interesting-papers/general/chapter_figure.pdf