Recent developments in the area of image mining have shown the way for incredible growth in
extensively large and detailed image databases. The images which are available in these
databases, if checked, can endow with valuable information to the human users. As one of the
most successful applications of image analysis and understanding, fac
e recognition has
recently gained important attention particularly throughout the past many years. Though
tracking and recognizing face objects is a routine task, building such a system is still an active
research. Among several proposed face rec
ognition schemes, shape based approaches are
possibly the most promising ones. This paper provides an overview of various
classification and retrieval methods that were proposed earlier in literature. Also, this paper
provides a margina
l summary for future research and enhancements in face detection
Cross Pose Facial Recognition Method for Tracking any Person's Location an Ap...ijtsrd
In todays world, there are number of existing methods for facial recognition. These methods are based on frontal view face data. There are few methods which are based on non-frontal view face recognition method. In most of the face recognition algorithm, œFeature space approach is used. In this approach, different feature vectors are extracted from face. These distances are compared to determine matches. In this paper, it is proposed that how any person can be located in a campus or in a city using a cross pose face recognition method. This paper is focusing on three parts 1) generation of multi-view images 2) comparison of images 3) showing the actual location of a person. Sanjay D. Sawaitul"Cross Pose Facial Recognition Method for Tracking any Persons Location an Approach" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7186.pdf http://www.ijtsrd.com/computer-science/data-processing/7186/cross-pose-facial-recognition-method-for--tracking-any-persons-location-an-approach/sanjay-d-sawaitul
Scale Invariant Feature Transform Based Face Recognition from a Single Sample...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Face recognition is the ability of categorize a set of images based on certain discriminatory features. Classification of the recognition patterns can be difficult problem and it is still very active field of research. The paper introduces conceptual framework for descriptive study on techniques of face recognition systems. It aims to describe the previous researches have been study the face recognition system, in order scope on the algorithms, usages, benefits , challenges and problems in this felids, the paper proposed the face recognition as sensitive learning task experiments on a large face databases demonstrate of the new feature. The researcher recommends that there's a needs to evaluate the previous studies and researches, especially on face recognition field and 3D, hopeful for advanced techniques and methods in the near future.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Review of facial expression recognition system and used datasetseSAT Journals
Abstract The human face is main part to recognize the individuals as well as provides the important information, current state of user behavior through their different expressions. Therefore, in biometric area of the research, automatically face & face expression recognition attracts researcher’s interest. The other areas which use such technique are computer science medicine, psychology etc. Usually face recognition system is consisting of many internal tasks. Face detection is thefirst task of such systems. Due to different variations across the human faces, the process of detecting face becomes complex. But with help of different modeling methods, it becomes possible to recognize the face and hence different face expressions. This paperpresents a literature review over the techniques and methods used for facial expression recognition. Also, different facial expression datasets available for the research or testing of existing methods of facial expression recognition are discussed. Keywords: Facial Expression, Face Detection, Features Extraction, Recognition, datasets.
Cross Pose Facial Recognition Method for Tracking any Person's Location an Ap...ijtsrd
In todays world, there are number of existing methods for facial recognition. These methods are based on frontal view face data. There are few methods which are based on non-frontal view face recognition method. In most of the face recognition algorithm, œFeature space approach is used. In this approach, different feature vectors are extracted from face. These distances are compared to determine matches. In this paper, it is proposed that how any person can be located in a campus or in a city using a cross pose face recognition method. This paper is focusing on three parts 1) generation of multi-view images 2) comparison of images 3) showing the actual location of a person. Sanjay D. Sawaitul"Cross Pose Facial Recognition Method for Tracking any Persons Location an Approach" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7186.pdf http://www.ijtsrd.com/computer-science/data-processing/7186/cross-pose-facial-recognition-method-for--tracking-any-persons-location-an-approach/sanjay-d-sawaitul
Scale Invariant Feature Transform Based Face Recognition from a Single Sample...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Face recognition is the ability of categorize a set of images based on certain discriminatory features. Classification of the recognition patterns can be difficult problem and it is still very active field of research. The paper introduces conceptual framework for descriptive study on techniques of face recognition systems. It aims to describe the previous researches have been study the face recognition system, in order scope on the algorithms, usages, benefits , challenges and problems in this felids, the paper proposed the face recognition as sensitive learning task experiments on a large face databases demonstrate of the new feature. The researcher recommends that there's a needs to evaluate the previous studies and researches, especially on face recognition field and 3D, hopeful for advanced techniques and methods in the near future.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Review of facial expression recognition system and used datasetseSAT Journals
Abstract The human face is main part to recognize the individuals as well as provides the important information, current state of user behavior through their different expressions. Therefore, in biometric area of the research, automatically face & face expression recognition attracts researcher’s interest. The other areas which use such technique are computer science medicine, psychology etc. Usually face recognition system is consisting of many internal tasks. Face detection is thefirst task of such systems. Due to different variations across the human faces, the process of detecting face becomes complex. But with help of different modeling methods, it becomes possible to recognize the face and hence different face expressions. This paperpresents a literature review over the techniques and methods used for facial expression recognition. Also, different facial expression datasets available for the research or testing of existing methods of facial expression recognition are discussed. Keywords: Facial Expression, Face Detection, Features Extraction, Recognition, datasets.
Review of face detection systems based artificial neural networks algorithmsijma
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 recogition from a single sample using rlog filter and manifold analysisacijjournal
Face
recognition is A technique that has been widely used in various important field, this process helps
in
the identification of an individual by a machine for the purpose of security and ease of work. The n
ormal
technique of face recognition usually works bet
ter when there are multiple samples for a single person
(MSSP) is available. In present applications where this technique is to be used such as in social ne
tworks,
security systems, identification cards there is only a single sample per person (SSPP) that
is readily
available. This less availability of the samples causes failure in the working of conventional face
recognition techniques which require multiple samples for a particular individual. To overcome this
drawback which sets back the system from the
accurate functioning of face recognition this paper puts
forward a novel technique which makes use of discriminative multi
-
manifold analysis (DMMA) that
extracts distinctive features using image patches. Recognition is done by the process of manifold to
ma
nifold matching. Hence there is an increment in the accuracy rate of face recognition.
A Hybrid Approach to Face Detection And Feature Extractioniosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A NOVEL BINNING AND INDEXING APPROACH USING HAND GEOMETRY AND PALM PRINT TO E...ijcsa
This paper proposes a Bio metric identification system for person identification using two bio metric traits
hand geometry and palm print. The hand image captured from digital camera is preprocessed to identify
key points on palm region of hand. Identified key points are used to find hand geometry feature and palm
print Region of interest (ROI). The discriminative palm print features are extracted by applying local
binary descriptor on palm print ROI. In a bio metric identification system the identity corresponding to the
input image (probe) is determined by comparing probe template with the templates of all identities enrolled
in biometric system (gallery). Response time to establish the identity of an individual increases in proportion to the number of enrollees. One way to reduce the response time is to retrieve a smaller set of candidate identity templates from the database for explicit comparison. In this paper we propose a coarseto-fine hierarchical approach to retrieve a smaller set of candidate identities called as candidate set to reduce the response time. The proposed approach is tested on the database collected at our institute.Proposed approach is of significance since hand geometry and palm print features can be extracted from the palm region of the hand. Also performance of identification system is enhanced by reducing the response time without compromising the identification accuracy.
The foundations for biometrics or identification systems were laid long ago. Today these developments have contributed to the identification of people, access to private sites and all places that need security and order with the help of computerized computers that perform biometric facial recognition, exclusively based on images of human faces for their function. With the extraction of facial midst characteristics of each person provides information used for the detection of the face. This communication also addresses the different processes, stages and methods of feature extraction operated by facial recognition systems. Including the positive and negative aspects of the implementation of these, the advantages and disadvantages, peoples criteria in this respect. Tovbaev Sirojiddin | Karshiboev Nizomiddin "Image Based Facial Recognition" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31330.pdf Paper Url :https://www.ijtsrd.com/computer-science/artificial-intelligence/31330/image-based-facial-recognition/tovbaev-sirojiddin
AHP validated literature review of forgery type dependent passive image forge...IJECEIAES
Nowadays, a lot of significance is given to what we read today: newspapers, magazines, news channels, and internet media, such as leading social networking sites like Facebook, Instagram, and Twitter. These are the primary wellsprings of phony news and are frequently utilized in malignant manners, for example, for horde incitement. In the recent decade, a tremendous increase in image information generation is happening due to the massive use of social networking services. Various image editing software like Skylum Luminar, Corel PaintShop Pro, Adobe Photoshop, and many others are used to create, modify the images and videos, are significant concerns. A lot of earlier work of forgery detection was focused on traditional methods to solve the forgery detection. Recently, Deep learning algorithms have accomplished high-performance accuracies in the image processing domain, such as image classification and face recognition. Experts have applied deep learning techniques to detect a forgery in the image too. However, there is a real need to explain why the image is categorized under forged to understand the algorithm’s validity; this explanation helps in mission-critical applications like forensic. Explainable AI (XAI) algorithms have been used to interpret a black box’s decision in various cases. This paper contributes a survey on image forgery detection with deep learning approaches. It also focuses on the survey of explainable AI for images.
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.
Face Detection in Digital Image: A Technical ReviewIJERA Editor
Face detection is the method of focusing faces in input image is an important part of any face processing system. In Face detection, segmentation plays the major role to detect the face. There are many contests for effective and efficient face detection. The aim of this paper is to present a review on several algorithms and methods used for face detection. We read the various surveys and related various techniques according to how they extract features and what learning algorithms are adopted for. Face detection system has two major phases, first to segment skin region from an image and second to decide these regions cover human face or not. There are number of algorithms used in face detection namely Genetic, Hausdorff Distance etc.
Presentamos varias butacas para este año 2014, pensadas para estadios, colectividades, usos múltiples, etc. ideales para exteriores e interiores.
We present several seats for this year 2014, designed for stadiums, groups, multiple uses, etc.. Is ideal for indoor and outdoor spaces.
Review of face detection systems based artificial neural networks algorithmsijma
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 recogition from a single sample using rlog filter and manifold analysisacijjournal
Face
recognition is A technique that has been widely used in various important field, this process helps
in
the identification of an individual by a machine for the purpose of security and ease of work. The n
ormal
technique of face recognition usually works bet
ter when there are multiple samples for a single person
(MSSP) is available. In present applications where this technique is to be used such as in social ne
tworks,
security systems, identification cards there is only a single sample per person (SSPP) that
is readily
available. This less availability of the samples causes failure in the working of conventional face
recognition techniques which require multiple samples for a particular individual. To overcome this
drawback which sets back the system from the
accurate functioning of face recognition this paper puts
forward a novel technique which makes use of discriminative multi
-
manifold analysis (DMMA) that
extracts distinctive features using image patches. Recognition is done by the process of manifold to
ma
nifold matching. Hence there is an increment in the accuracy rate of face recognition.
A Hybrid Approach to Face Detection And Feature Extractioniosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A NOVEL BINNING AND INDEXING APPROACH USING HAND GEOMETRY AND PALM PRINT TO E...ijcsa
This paper proposes a Bio metric identification system for person identification using two bio metric traits
hand geometry and palm print. The hand image captured from digital camera is preprocessed to identify
key points on palm region of hand. Identified key points are used to find hand geometry feature and palm
print Region of interest (ROI). The discriminative palm print features are extracted by applying local
binary descriptor on palm print ROI. In a bio metric identification system the identity corresponding to the
input image (probe) is determined by comparing probe template with the templates of all identities enrolled
in biometric system (gallery). Response time to establish the identity of an individual increases in proportion to the number of enrollees. One way to reduce the response time is to retrieve a smaller set of candidate identity templates from the database for explicit comparison. In this paper we propose a coarseto-fine hierarchical approach to retrieve a smaller set of candidate identities called as candidate set to reduce the response time. The proposed approach is tested on the database collected at our institute.Proposed approach is of significance since hand geometry and palm print features can be extracted from the palm region of the hand. Also performance of identification system is enhanced by reducing the response time without compromising the identification accuracy.
The foundations for biometrics or identification systems were laid long ago. Today these developments have contributed to the identification of people, access to private sites and all places that need security and order with the help of computerized computers that perform biometric facial recognition, exclusively based on images of human faces for their function. With the extraction of facial midst characteristics of each person provides information used for the detection of the face. This communication also addresses the different processes, stages and methods of feature extraction operated by facial recognition systems. Including the positive and negative aspects of the implementation of these, the advantages and disadvantages, peoples criteria in this respect. Tovbaev Sirojiddin | Karshiboev Nizomiddin "Image Based Facial Recognition" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31330.pdf Paper Url :https://www.ijtsrd.com/computer-science/artificial-intelligence/31330/image-based-facial-recognition/tovbaev-sirojiddin
AHP validated literature review of forgery type dependent passive image forge...IJECEIAES
Nowadays, a lot of significance is given to what we read today: newspapers, magazines, news channels, and internet media, such as leading social networking sites like Facebook, Instagram, and Twitter. These are the primary wellsprings of phony news and are frequently utilized in malignant manners, for example, for horde incitement. In the recent decade, a tremendous increase in image information generation is happening due to the massive use of social networking services. Various image editing software like Skylum Luminar, Corel PaintShop Pro, Adobe Photoshop, and many others are used to create, modify the images and videos, are significant concerns. A lot of earlier work of forgery detection was focused on traditional methods to solve the forgery detection. Recently, Deep learning algorithms have accomplished high-performance accuracies in the image processing domain, such as image classification and face recognition. Experts have applied deep learning techniques to detect a forgery in the image too. However, there is a real need to explain why the image is categorized under forged to understand the algorithm’s validity; this explanation helps in mission-critical applications like forensic. Explainable AI (XAI) algorithms have been used to interpret a black box’s decision in various cases. This paper contributes a survey on image forgery detection with deep learning approaches. It also focuses on the survey of explainable AI for images.
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.
Face Detection in Digital Image: A Technical ReviewIJERA Editor
Face detection is the method of focusing faces in input image is an important part of any face processing system. In Face detection, segmentation plays the major role to detect the face. There are many contests for effective and efficient face detection. The aim of this paper is to present a review on several algorithms and methods used for face detection. We read the various surveys and related various techniques according to how they extract features and what learning algorithms are adopted for. Face detection system has two major phases, first to segment skin region from an image and second to decide these regions cover human face or not. There are number of algorithms used in face detection namely Genetic, Hausdorff Distance etc.
Presentamos varias butacas para este año 2014, pensadas para estadios, colectividades, usos múltiples, etc. ideales para exteriores e interiores.
We present several seats for this year 2014, designed for stadiums, groups, multiple uses, etc.. Is ideal for indoor and outdoor spaces.
Learning to understand our fellow members and leaders is one of the best things we can do in Toastmasters to maintain club membership. This presentation speaks to one of the most effective ways to accomplish that goal. While this deck was prepared for Toastmasters, it may be applied in many other business and personal environments.
This deck was presented at the Toastmaster's District 45 Fall Conference on Prince Edward Island, Canada on October 25, 2014.
Since the presentation, I noticed that TM has updated the survey with 2014 data. It can be found here: http://bit.ly/1FwRRoE
AN IMPROVED ITERATIVE METHOD FOR SOLVING GENERAL SYSTEM OF EQUATIONS VIA GENE...Zac Darcy
Various algorithms are known for solving linear system of equations. Iteration methods for solving the
large sparse linear systems are recommended. But in the case of general n× m matrices the classic
iterative algorithms are not applicable except for a few cases. The algorithm presented here is based on the
minimization of residual of solution and has some genetic characteristics which require using Genetic
Algorithms. Therefore, this algorithm is best applicable for construction of parallel algorithms. In this
paper, we describe a sequential version of proposed algorithm and present its theoretical analysis.
Moreover we show some numerical results of the sequential algorithm and supply an improved algorithm
and compare the two algorithms.
Mastermail zomer 2014 - nieuw
Het thema van het Mastermail magazine uitgave zomer 2014 (looptijd voor de acties is van 1 juni 2014 t/m 31 augustus 2014) is 'Regelgeving - vriend of vijand, er is altijd een oplossing'. Lees de uitleg over de nieuwe Wet maatschappelijke ondersteuning op pagina 2 en 3.
Op pagina 2 is ook een verwijzing opgenomen over de 'Huistest', een initiatief waarmee aannemers en ouderen samen praktische verbeterpunten aan woningen kunnen bekijken met als doel om de woning levensloopbestendig te kunnen maken.
Er staan ook weer twee mooie interviews in: een interview met Koopmans JP van Eesteren bij het bouwproject Gemeentehuis in Almelo (meer weten over Koopmans, kijk dan op www.koopmans.nl) en met Van Mierlo Bouw & Ontwikkeling (meer weten over Van Mierlo, kijk dan op www.vmierlo.nl) over zijn samenwerking met Mastermate.
Bekijk ook de actie-artikelen in de Mastermail.
On Approach of Estimation Time Scales of Relaxation of Concentration of Charg...Zac Darcy
In this paper we generalized recently introduced approach for estimation of time scales of mass transport.
The approach have been illustrated by estimation of time scales of relaxation of concentrations of charge
carriers in high-doped semiconductor. Diffusion coefficients and mobility of charge carriers and electric
field strength in semiconductor could be arbitrary functions of coordinate.
2013. gada 6. jūnijā Rīgā norisinājās Finanšu ministrijas organizēta starptautiska konference „Fiskālās politikas perspektīvas Latvijā un Eiropas Savienībā”.
Konferencē diskutēja par fiskālās politikas reformām, kas ir viens no aktuālākajiem jautājumiem Eiropas politikas darba kārtībā. Konferences dalībnieki tika iepazīstināti arī ar pašreizējām tendencēm Baltijas valstu publiskajās finansēs un turpmākā darba prioritātēm.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Innovative Analytic and Holistic Combined Face Recognition and Verification M...ijbuiiir1
Automatic recognition and verification of human faces is a significant problem in the development and application of Human Computer Interaction (HCI).In addition, the demand for reliable personal identification in computerized access control has resulted in an increased interest in biometrics to replace password and identification (ID) card. Over the last couple of years, face recognition researchers have been developing new techniques fuelled by the advances in computer vision techniques, Design of computers, sensors and in fast emerging face recognition systems. In this paper, a Face Recognition and Verification System has been designed which is robust to variations of illumination, pose and facial expression but very sensitive to variations of the features of the face. This design reckons in the holistic or global as well as the analyticor geometric features of the face of the human beings. The global structure of the human face is analysed by Principal Component Analysis while the features of the local structure are computed considering the geometric features of the face such as the eyes, nose and the mouth. The extracted local features of the face are trained and later tested using Artificial Neural Network (ANN). This combined approach of the global and the local structure of the face image is proved very effective in the system we have designed as it has a correct recognition rate of over 90%.
Face recognition is a widely used biometric approach. Face recognition technology has developed rapidly
in recent years and it is more direct, user friendly and convenient compared to other methods. But face
recognition systems are vulnerable to spoof attacks made by non-real faces. It is an easy way to spoof face
recognition systems by facial pictures such as portrait photographs. A secure system needs Liveness
detection in order to guard against such spoofing. In this work, face liveness detection approaches are
categorized based on the various types techniques used for liveness detection. This categorization helps
understanding different spoof attacks scenarios and their relation to the developed solutions. A review of
the latest works regarding face liveness detection works is presented. The main aim is to provide a simple
path for the future development of novel and more secured face liveness detection approach.
Face Liveness Detection for Biometric Antispoofing Applications using Color T...rahulmonikasharma
Face recognition is a widely used biometric approach. Face recognition technology has developed rapidly in recent years and it is more direct, user friendly and convenient compared to other methods. But face recognition systems are vulnerable to spoof attacks made by non-real faces. It is an easy way to spoof face recognition systems by facial pictures such as portrait photographs. A secure system needs Liveness detection in order to guard against such spoofing. In this work, face liveness detection approaches are categorized based on the various types techniques used for liveness detection. This categorization helps understanding different spoof attacks scenarios and their relation to the developed solutions. A review of the latest works regarding face liveness detection works is presented. The main aim is to provide a simple path for the future development of novel and more secured face liveness detection approach.
Now days, the task of face recognition is widely used application of image analysis as well as pattern recognition. In biometric area of the research, automatically face & face expression recognition attracts researcher’s interest. For classifying facial expressions into different categories, it is necessary to extract important facial features which contribute in identifying proper and particular expressions. Recognition and classification of human facial expression by computer is an important issue to develop automatic facial expression recognition system in vision community. In this paper the facial expression recognition system is proposed.
Face Recognition plays a major role in Biometrics. Feature selection is a measure issue in face
recognition. This paper proposes a survey on face recognition. There are many methods to extract face
features. In some advanced methods it can be extracted faster in a single scan through the raw image and
lie in a lower dimensional space, but still retaining facial information efficiently. The methods which are
used to extract features are robust to low-resolution images. The method is a trainable system for selecting
face features. After the feature selection procedure next procedure is matching for face recognition. The
recognition accuracy is increased by advanced methods.
Comparative Studies for the Human Facial Expressions Recognition Techniquesijtsrd
This article reviews the different techniques for recognizing facial expressions. First, it gives a description of the emotions their types and the techniques to measure the emotions. Then it talks about the identification of the face and then the techniques for extracting the features from the face. Then the various classifiers designed to classify these extracted features are discussed. Finally, a comparative study of some of the recent studies has been presented. Sheena Gaur | Mayank Dixit | Sayed Nasir Hasan | Anwaar Wani | Tanveer Kazi | Ahsan Z Rizvi "Comparative Studies for the Human Facial Expressions Recognition Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd28027.pdfPaper URL: https://www.ijtsrd.com/computer-science/other/28027/comparative-studies-for-the-human-facial-expressions-recognition-techniques/sheena-gaur
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Facial image classification and searching –a survey
1. International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 2, No.2, May 2014
DOI : 10.5121/ijitmc.2014.2203 19
FACIAL IMAGE CLASSIFICATION AND
SEARCHING –A SURVEY
Dr.S.Vijayarani1
and Mrs.M.Vinupriya2
1 Assistant Professor, Department of Computer Science, School of Computer
Science and Engineering, Bharathiar University, Coimbatore -641 046, Tamil
Nadu
2 Research Scholar, Department of Computer Science, School of Computer
Science and Engineering, Bharathiar University, Coimbatore - 641 046,
Tamil Nadu
ABSTRACT
Recent developments in the area of image mining have shown the way for incredible growth in
extensively large and detailed image databases. The images which are available in these
databases, if checked, can endow with valuable information to the human users. As one of the
most successful applications of image analysis and understanding, face recognition has
recently gained important attention particularly throughout the past many years. Though
tracking and recognizing face objects is a routine task, building such a system is still an active
research. Among several proposed face recognition schemes, shape based approaches are
possibly the most promising ones. This paper provides an overview of various
classification and retrieval methods that were proposed earlier in literature. Also, this paper
provides a marginal summary for future research and enhancements in face detection.
KEYWORDS
Image mining, face detection, classification, retrieval, SVM technique, searching.
1. INTRODUCTION
Data mining is a process which uses a mixture of data analysis tools to discover
patterns and relationships in data that may be used to make valid predictions. It permits
users to analyze data from many different dimensions or angles, classify it, and
summarize the relationships identified. Technically, data mining is the method of
identifying correlations or patterns among dozens of fields in massive relative
databases. Some of the research areas in data mining are web mining, text mining, data
streams, image mining, sequence mining and multimedia mining. Image mining
facilitates the abstraction of hidden information which is not clearly accrued in the
image. It is used to detect unfamiliar patterns and abstract inherent and useful data
from images stored in the large databases. Therefore image mining deals with making
relationships between different images from large image databases. A large amount of
characteristics used for image category are: shape, texture, color and spatial features.
2. International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 2, No.2, May 2014
20
Image mining is not only a limb of data mining and knowledge discovery, but also an
highly developed research area which includes digital image processing, image
indexing and retrieval, image understanding, database, artificial intelligence, pattern
discovery and so on[7].
Research in image mining can be broadly classified in two main directions [6] (1)
Domain- specific applications where the focus is in the process of extracting the most
applicable image features into a form appropriate for data mining. (2) General
applications where the focus is to create the image patterns that may be helpful in the
understanding of the communication
between high-level human perceptions of image and low level image features. Both are
used to extract most relevant image feature and later to generate image patterns. A
number of image mining systems have been developed for different applications which
include remote sensing, natural scene recognition, weather forecasting, egeria
detection, criminal investigation, image segmentation, medical diagnosis, space
research, biology, image classification and retrieval etc.
Face Recognition System is a computer application for automatically identifying or
verifying a person from a digital image or a single frame from a video source.
This can be done by comparing selected facial characteristics of the likeness and a
facial database. It executes that by comparing the face of the accessing user with a
database of faces previously stored in memory. Face recognition presents a challenging
problem in the area of image analysis and computer vision, and it has received a
great deal of interest over the last few years because of its applications in
different domains. There are many problems that exist due to many factors that can
affect the images. When processing images one must take into account the variations
in lightweight, image quality, the individuals pose and facial expressions along with
others.
The rest of the paper is organized as follows. Section 2 describes the different
approaches of image based face recognition and ITS applications. Section 3 discusses
about the importance of classification and searching in facial images. Section 4
provides a literature survey. Section 5 shows various research directions involved in the
face recognition and conclusion is given in Section 6.
2. DIFFERENT APPROACHES AND APPLICATIONS IN
FACE RECOGNITION
Different types of facial images are taken for face detection. The images that are
collected in a semi-controlled environment are used as input. Images were taken in
uncontrolled indoor environment and different facial expressions (such as open / closed
eyes, smiling / not smiling) or configurations (such as w/glasses, center-light, happy,
left-light, normal, right-light, w/no glasses, sad, sleepy, surprised, and wink). The
images are taken by varying the lighting and facial details (glasses / no glasses) at
different times and are used as input.
3. International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 2, No.2, May 2014
21
2.1. Different approaches
There are numerous ways to detect a face in a scene and it is easier and harder ones.
Here is a list of the most familiar approaches in face detection:
Identifying faces in images with prohibited background
Identifying faces by motion
Using a combination of the above
Identifying faces in unconstrained scenes
Figure 1: Taxonomy of Iamge based Face Recognition Approaches
The existing techniques are reviewed to detect faces from a single intensity or color
image. Single image detection methods are classified into four categories [11],
1. Knowledge-based methods. These rule-based procedures encode human
information of what constitutes a distinctive face. Usually, the rules confine the
relationships between facial features. These procedures are designed mainly for face
localization. Knowledge based methods can be studied by two different approaches;
which are top-down methods and bottom-up methods.
2. Feature invariant approaches. These algorithms intend to find structural
features that live even when the pose, viewpoint, or lighting conditions differ, and then
use these to find faces. These methods are designed mainly for face localization.
3. Template matching methods. There are number of standard patterns of a face
are stored which is used to describe the face as a whole or the facial features separately.
The associations between an input image and the stored patterns are worked out for
detection. These methods have been applied for both face localization and detection.
Template matching researches can be divided into two subcategories which are,
researches using predefined templates and the other is researches using deformable
templates.
4. Appearance-based methods. In difference to template matching, the
models (or templates) are first learned from a set of training images which should
capture the representative variability of facial appearance. These learned models are
4. International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 2, No.2, May 2014
22
then utilised for detection. These methods are designed largely for face detection.
There are number of approaches using the appearance based methods are,
eigenfaces, distribution-based methods, neural networks, support vector machines,
sparse network of winnows, Naïve Bayes classifiers, hidden Markov model ,
information theoretical approach and inductive learning.
2.2APPLICATIONS
Face recognition is utilised for two primary jobs:
1. Verification (one-to-one matching): When offered with a face image of an
unidentified individual along with a claim of individuality, ascertaining whether the
individual is who he/she claims to be.
2. Identification (one-to-many matching): When a system offered with an image
of an unidentified individual, determining that person’s identity by matching
(possibly after encoding) that image with a database of (possibly encoded) images of
identified individuals.
There are several application areas in which face recognition can be exploited for these
two reasons, a few of which are outlined below.
Security: It is mostly utilised for accessing controls to airports/seaports,
buildings, airports/seaports, ATM machines and border checkpoints [8];
computer/ network security [4]; email authentication on multimedia
workstations.
Surveillance: It can be seen with a large number of CCTVs can be examined to
look for identified criminals,drug offenders, etc. and authorities can be notified
when one is to be found; for instance, this procedure was used at the Super
Bowl 2001 game at Tampa, Florida [3];
General identity verification is done for electoral registration, electronic
commerce, banking, identifying newborns, passports, national IDs, drivers’
licenses, workers IDs, etc.
Criminal justice systems such as mug-shot/booking systems, post-event
investigation, forensics.
Image database investigations: used for searching image databases of licensed
drivers, promote recipients, immigrants, missing children and police bookings.
“Smart Card” applications: In the maintenance of facial images database, the
face-print
can be stored in a bar code, smart card or magnetic stripe, authentication of which is
achieved by matching the live image and the retained template [18].
5. International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 2, No.2, May 2014
23
Multi-media environments with adaptive individual computer interfaces
a part of ubiquitous or context aware systems, behavior examining at
childcare or old people’s centers, identifying a customer and assessing his
needs [16, 8].
3. FACIAL IMAGE CLASSIFICATION AND SEARCHING
Image classification intends to find a description that can best describe the images in
one class and to distinguish these images from all the other classes. It is one of the most
often used methods for extracting information from images. Classification involves verdict
rules that partition the data into disjoint groups. Training data set is the input for the
classification data set, whose class labels are already known. In Classification, usually
several features are used for a set of pixels i.e., many images of a particular object are
needed. Image search engines are currently dependent on textual metadata. This
information can be in the form of filenames, images, manual annotations, or surrounding
text. However, for the huge bulk of images on the internet and in peoples’ private
collections, this data is often confusing, erroneous, or simply not present. This
presents a great opportunity to use attribute classifiers on images with faces, thereby
making them searchable. An image retrieval system is a computer system which is used for
searching, browsing and retrieving images from large set of databases. Many image retrieval
systems both business and research have been built. A large amount of image retrieval
systems support one or more of the following choices:
Random browsing
Searching through example
Searching through sketch
Searching through text (including key word or speech)
Steering with modified image categories.
4. RELATED WORKS
Several researches have been carried on this face detection. This section presents a study on
various classification and retrieval methods that were proposed earlier.
S.Ravi, S.Wilson [15] proposed a color model conversion algorithm based on
chrominance color, this information is used for detecting face region. The facial features
such as eyes, nose, and mouth are used to find the region of the feature pixel by applying
threshold measurements. The algorithm provides a better accuracy for classifying the gender
using SVM classification algorithm. The main issue involved is choosing the correct
threshold parameter for facial feature detection and gender classification, which clearly
might be inadequate for the data.
Lin-Lin Huanga, AkinobuShimizu, Yoshihoro Hagihara, Hidefumi Kobatake [9], proposed
a classification-based approach for locating frontal and nearly frontal faces in cluttered
images. The directional decomposition of gradient provides a better discrimination ability
6. International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 2, No.2, May 2014
24
than the image intensity and the gradient map, and it was shown that the combination of
gradient
direction and intensity further improves the detection performance. To improve the
detection performance, gradient direction features from local window images are extracted
as input for underlying two-class classifier. Simple and complex background images are
taken to check the performance and it provides a better performance.
Yongmin Li, Shaogang Gong and Heather Liddell [19], discussed about the SVM
approach used for multi-view face detection and recognition. It involves the following
methods
1.SVM construct pose estimators which gives fast estimation of head pose. 2.
Symmetrical property of the face images are used to classify the half possible views with the
trained set. 3. The improvement of detection accuracy and easy computation, pose
information is used to guide the selection of face detectors by using pose change smoothing
technique.
Saman cooray, Noel O’ Connor [14], has discussed to detect frontal faces; the author had
used a technique that combines feature extraction and statistical face classification. The use
of eye facial feature points enables to derive a normalized search space by eliminating
the requirement of analyzing the image at multiple scales for detecting different sized faces.
Since the search space is normalized, the need for analyzing the image at each image pixel
location is eliminated. This in turn provides a promising approach for facial classification.
Bo WU, Haizhou AI and Chang HUANG [2], described a LUT weak classifier based
boosting method for face retrieval by demographic classification. A Haar feature based
2D LUT-type weak classifier was developed for multi-class problems and had used a
variation of boosting algorithm for multi-class multilabel problems, Adaboost.MH to learn
the demographic classifiers. A model of automatic demographic face retrieval system is
presented and the experimental results show its potentials in the management of a large
facial image database for online retrieval applications.
Ahmed Abdu Alattab, Sameem Abdul Kareem [1]: has discussed about the
integration of verbal description of human face and eigen face feature achieved excellent
results in the retrieval of face image when compared to retrieval by image content that
reduces the semantic gap between high level query requirement represented by user verbal
description and low level facial features represented by image content features. Combining
the two methods of query by description and query by image example, the accuracy of
the retrieval process is improved automatically and the needed time to find the desired
faces is also minimized. The idea was based on matching the user verbal description of the
query face with the annotated description of the faces in the database. The system then uses
eigen faces features for further searches on narrowed down search space of the pruned
set facial images, achieving more accurate results.
Yogesh R. Tayade1, Prof. S.M. Bansode2 [18], developed an efficient method for face
retrieval by merging three different algorithms SIFT, LBP and IDSC. An image is given as
an input, it is filtered and it is represented in a sparse matrix that derives SIFT and LBP
7. International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 2, No.2, May 2014
25
features. Through this work, the face images are successfully retrieved from the
trained dataset of Labeled Faces in Wild (LFW) images which competently achieves better
retrieval rate.
Yannis Avrithis, Nicolas Tsapatsoulis and Stefanos Kollias [17], portrays that to extract
objects from images color segmentation has proved to be a powerful tool, especially in the
case of human faces that are usually characterized by uniform color. The M-RSST algorithm
eliminates facial details and provides a single object for each face. Moreover,
chrominance components provided with a probabilistic model are used in an efficient way
for retrieving facial images from image databases. The interactive form of the proposed
system adapts the model to the needs of the user and consequently leads to much more
meaningful retrieval results.
Neeraj Kumar, et al., [12], discussed about how to automatically train classifiers for
describable aspects of visual appearance – attributes and similes. These classifiers are
learned using huge collections of labeled images obtained from the internet. They
demonstrated the use of these describable attributes for performing face verification and
image search. It was performing better in aspects such as, attribute classification, face
verification, and search (qualitatively).
5. RESEARCH DIRECTIONS
The challenges combined with face detection can be attributed to the following
factors
[8, 4, 10]:
1. Pose: The images of a face vary due to the relative camera-face pose (frontal, 45 degree,
profile, upside down), and some facial characteristics such as an eye or the nose may become
partly or wholly occluded.
2. Presence or absence of structural elements: Facial characteristics such as beards,
mustaches, and glasses may or may not be present and there is a great deal of variability
among these components including color, shape, and size.
3. Facial expression. The appearances of faces are straightly influenced by a person’s
facial expression.
4. Occlusion. Faces may be partly occluded by other objects. An image with a group of
people, some faces may partly occlude other faces.
5. Image orientation: Face images straightly differ for different rotations about the
camera’s optical axis.
6. Imaging conditions: When the image is produced, features such as lighting (spectra,
source distribution and intensity) and camera characteristics (sensor response, lenses)
affects the outer shell of a face.
7. The age: As the face matures, it modifies some of its most enduring properties (e.g.,
shape of cranium) and acquires new attributes (e.g., wrinkles,spot). These changes gives the
basic information about the aging of the face.
8. Changes in illumination: Person face was slower and less accurate at matching and
naming faces when there was a change in illumination direction.
9. International Journal of Information Technology, Modeling and Computing (IJITMC) Vol. 2, No.2, May 2014
27
[12] Neeraj Kumar, Student Member, IEEE, Alexander C. Berg, Member, IEEE, Peter N. Belhumeur, and
Shree K. Nayar, Member, IEEE “Describable Visual Attributes for Face Verification and
Image Search” IEEE Transactions On Pattern Analysis And Machine Intelligence.
[13] Rabia Jafri* and Hamid R. Arabnia* “A Survey of Face Recognition Techniques” Journal of
Information Processing Systems, Vol.5, No.2, June 2009
[14] Saman cooray, Noel O’ Connor Facial features and appearance-based classification for face
detection in color images.
[15] S.Ravi , S.Wilson Face Detection with Facial Features and Gender Classification Based On Support
Vector Machine 2010 Special Issue - International Journal of Imaging Science and Engineering
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Authors
Dr. S.Vijayarani has completed MCA, M.Phil and Ph.D in Computer
Science. She is working as Assistant Professor in the School of Computer
Science and Engineering, Bharathiar University, Coimbatore. Her fields of
research interest are data mining, privacy, security, bioinformatics and data
streams. She has published papers in the international journals and
presented research papers in international and national conferences.
Mrs. M.Vinupriya has completed MCA. She is currently pursuing her
M.Phil in Computer Science in the School of Computer Science and
Engineering,Bharathiar University, Coimbatore. Her fields of interest are
Image Mining in data mining and privacy preserving in Data mining.