IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
The International Journal of Engineering and Science (IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
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.
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.
The International Journal of Engineering and Science (IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Face Recognition Using Simplified Fuzzy Artmapsipij
Face recognition has become one of the most active research areas of pattern recognition since the early 1990s. This project thesis proposes a novel face recognition method based on Simplified Fuzzy ARTMAP (SFAM). For extracting features to be used for classification, combination of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) is used. This is for improving the capability of LDA and PCA when used alone.PCA reduces the dimensionality of input face images while LDA extracts the features that help the classifier to classify the input face images. The classifier employed was SFAM. Experiment is conducted on ORL, Yale and Indian Face Database and results demonstrate SFAM’s efficiency as a recognizer. The training time of SFAM is negligible. SFAM has the added advantage that the network is adaptive, that is, during testing phase if the network comes across a new face that it is not trained for; the network identifies this to be a new face and also learns this new face. Thus SFAM can be used in applications where database needs to be updated frequently. SFAM thus proves itself to be an efficient recognizer when a speedy, accurate and adaptive Face Recognition System is required.
Feature Extraction of Gesture Recognition Based on Image Analysis for Differe...IJERA Editor
Gesture recognition system received great attention in the recent few years because of its manifoldness applications and the ability to interact with machine efficiently through human computer interaction. Gesture is one of human body languages which are popularly used in our daily life. It is a communication system that consists of hand movements and facial expressions via communication by actions and sights. This research mainly focuses on the research of gesture extraction and finger segmentation in the gesture recognition. In this paper, we have used image analysis technologies to create an application by encoding in MATLAB program. We will use this application to segment and extract the finger from one specific gesture. This paper is aimed to give gesture recognition in different natural conditions like dark and glare condition, different distances condition and similar object condition then collect the results to calculate the successful extraction rate.
A comparison of stereo correspondence algorithms can be conducted by a quantitative evaluation of disparity maps. Among the existing evaluation methodologies, the Middlebury’s methodology is commonly used. However, the Middlebury’s methodology has shortcomings in the evaluation model and the error measure. These shortcomings may bias the evaluation results, and make a fair judgment about algorithms accuracy difficult. An alternative, the methodology is based on a multiobjective optimisation model that only provides a subset of algorithms with comparable accuracy. In this paper, a quantitative evaluation of disparity maps is proposed. It performs an exhaustive assessment of the entire set of algorithms. As innovative aspect, evaluation results are shown and analysed as disjoint groups of stereo correspondence algorithms with comparable accuracy. This innovation is obtained by a partitioning and grouping algorithm. On the other hand, the used error measure offers advantages over the error measure used in the Middlebury’s methodology. The experimental validation is based on the Middlebury’s test-bed and algorithms repository. The obtained results show seven groups with different accuracies. Moreover, the top-ranked stereo correspondence algorithms by the Middlebury’s methodology are not necessarily the most accurate in the proposed methodology
A comparison of stereo correspondence algorithms can be conducted by a quantitative evaluation of
disparity maps. Among the existing evaluation methodologies, the Middlebury’s methodology is commonly
used. However, the Middlebury’s methodology has shortcomings in the evaluation model and the error
measure. These shortcomings may bias the evaluation results, and make a fair judgment about algorithms
accuracy difficult. An alternative, the A* methodology is based on a multiobjective optimisation model that
only provides a subset of algorithms with comparable accuracy. In this paper, a quantitative evaluation of
disparity maps is proposed. It performs an exhaustive assessment of the entire set of algorithms. As
innovative aspect, evaluation results are shown and analysed as disjoint groups of stereo correspondence
algorithms with comparable accuracy. This innovation is obtained by a partitioning and grouping algorithm.
On the other hand, the used error measure offers advantages over the error measure used in the
Middlebury’s methodology. The experimental validation is based on the Middlebury’s test-bed and
algorithms repository. The obtained results show seven groups with different accuracies. Moreover, the topranked
stereo correspondence algorithms by the Middlebury’s methodology are not necessarily the most
accurate in the proposed methodology.
Face Recognition for Different Facial Expressions Using Principal Component a...AM Publications
The face is our primary focus of attention in social intercourse, playing a major role in conveying
identity and emotion. We can recognize thousands of faces learned throughout our lifetime and identify familiar faces
at a glance even after years of separation. This skill is quite robust, despite large changes in the visual stimulus due to
viewing conditions, expression, aging, and distractions such as glasses, beards, changes in hairstyle. Though human
faces are complex in shape, face recognition is not difficult for a human brain whereas for a computer this job is not
easy. In this paper presents and analyzes the performance of Principle Component Analysis (PCA) based technique for
face recognition. We consider recognition of human faces with two facial expressions: single and differential. The
images that are captured previously constitute the training set. From these images eigenfaces are calculated. The image
that is going to be recognized through our system is mapped to the same eigenspaces. Next I used classification
technique namely distance based used to classify the images as recognized or non-recognized. Presently I got result for
the single facial expression now I am working for different facial expression.
Multiple features based fingerprint identification systemeSAT Journals
Abstract Security has become major issue now a day. In order to prevent unauthorized access of confidential data there is a need for accurate and reliable personal identification system. So, biometric based identification system is one of the best solutions. Fingerprint based system is one of oldest biometric identification systems. It is used in many commercial and security applications. Even with advent of technology in fingerprint identification system, the accurate extraction and matching of features from a fingerprint image is a challenging task. The task is much more challenging when fingerprint is affected by non-linear deformations such as rotation and translation. In this paper, fingerprint identification system using improved feature vector based algorithm is presented. In the algorithm Gabor filter is implemented to enhance the fingerprint image. The salient features minutiae (ridge endings) and reference point are extracted from the image. The Euclidian distances between reference point and each minutiae point are calculated and are arranged in ascending order. These are stored in database as feature vectors. The fingerprint matching is done based on the similarity rate between the feature vector of input fingerprint and the feature vectors stored in the database. Algorithms are implemented using Visual Studio 2010 in C++ language using Open CV libraries and tested on the fingerprint database created in the laboratory. Key Words: Fingerprint, Minutiae, Reference point, Euclidian distance, Similarity rate, Identification
A HYBRID COPY-MOVE FORGERY DETECTION TECHNIQUE USING REGIONAL SIMILARITY INDICESijcsit
Different methods have been experimented for processing and detecting forgery in digital images. Image forgery involves various activities like copy-move forgery, image slicing, retouching, morphing etc. In copy-move forgery a portion within the image is copied and pasted on another part of the same image,generally to conceal or enhance certain portions of the image. This paper proposes a copy-move forgery detection using local fractal dimension and structural similarity indices. The image is classified into different texture regions based on the local fractal dimension. Forgery checking is thus confined to be among the portions within a region. Structural similarity index measure is applied to each block pair in each region to localize the forged portion. Experimental results prove that this hybrid method can effectively detect such kind of image tampering with minimum false positives.
Visual Cryptography for biometric privacywaseem ahmad
visual cryptography for biometric privacy Preserving the privacy of digital biometric data (e.g., face images) stored in a central database has become of paramount importance. This work explores the possibility of using visual cryptography for imparting privacy to biometric data
Authentication of a person is the major concern in this era for security purposes. In biometric systems Signature is one of the behavioural features used for the authentication purpose. In this paper we work on the offline signature collected through different persons. Morphological operations are applied on these signature images with Hough transform to determine regular shape which assists in authentication process. The values extracted from this Hough space is used in the feed forward neural network which is trained using back-propagation algorithm. After the different training stages efficiency found above more than 95%. Application of this system will be in the security concerned fields, in the defence security, biometric authentication, as biometric computer protection or as method of the analysis of person’s behaviour changes.
Face Recognition Using Simplified Fuzzy Artmapsipij
Face recognition has become one of the most active research areas of pattern recognition since the early 1990s. This project thesis proposes a novel face recognition method based on Simplified Fuzzy ARTMAP (SFAM). For extracting features to be used for classification, combination of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) is used. This is for improving the capability of LDA and PCA when used alone.PCA reduces the dimensionality of input face images while LDA extracts the features that help the classifier to classify the input face images. The classifier employed was SFAM. Experiment is conducted on ORL, Yale and Indian Face Database and results demonstrate SFAM’s efficiency as a recognizer. The training time of SFAM is negligible. SFAM has the added advantage that the network is adaptive, that is, during testing phase if the network comes across a new face that it is not trained for; the network identifies this to be a new face and also learns this new face. Thus SFAM can be used in applications where database needs to be updated frequently. SFAM thus proves itself to be an efficient recognizer when a speedy, accurate and adaptive Face Recognition System is required.
Feature Extraction of Gesture Recognition Based on Image Analysis for Differe...IJERA Editor
Gesture recognition system received great attention in the recent few years because of its manifoldness applications and the ability to interact with machine efficiently through human computer interaction. Gesture is one of human body languages which are popularly used in our daily life. It is a communication system that consists of hand movements and facial expressions via communication by actions and sights. This research mainly focuses on the research of gesture extraction and finger segmentation in the gesture recognition. In this paper, we have used image analysis technologies to create an application by encoding in MATLAB program. We will use this application to segment and extract the finger from one specific gesture. This paper is aimed to give gesture recognition in different natural conditions like dark and glare condition, different distances condition and similar object condition then collect the results to calculate the successful extraction rate.
A comparison of stereo correspondence algorithms can be conducted by a quantitative evaluation of disparity maps. Among the existing evaluation methodologies, the Middlebury’s methodology is commonly used. However, the Middlebury’s methodology has shortcomings in the evaluation model and the error measure. These shortcomings may bias the evaluation results, and make a fair judgment about algorithms accuracy difficult. An alternative, the methodology is based on a multiobjective optimisation model that only provides a subset of algorithms with comparable accuracy. In this paper, a quantitative evaluation of disparity maps is proposed. It performs an exhaustive assessment of the entire set of algorithms. As innovative aspect, evaluation results are shown and analysed as disjoint groups of stereo correspondence algorithms with comparable accuracy. This innovation is obtained by a partitioning and grouping algorithm. On the other hand, the used error measure offers advantages over the error measure used in the Middlebury’s methodology. The experimental validation is based on the Middlebury’s test-bed and algorithms repository. The obtained results show seven groups with different accuracies. Moreover, the top-ranked stereo correspondence algorithms by the Middlebury’s methodology are not necessarily the most accurate in the proposed methodology
A comparison of stereo correspondence algorithms can be conducted by a quantitative evaluation of
disparity maps. Among the existing evaluation methodologies, the Middlebury’s methodology is commonly
used. However, the Middlebury’s methodology has shortcomings in the evaluation model and the error
measure. These shortcomings may bias the evaluation results, and make a fair judgment about algorithms
accuracy difficult. An alternative, the A* methodology is based on a multiobjective optimisation model that
only provides a subset of algorithms with comparable accuracy. In this paper, a quantitative evaluation of
disparity maps is proposed. It performs an exhaustive assessment of the entire set of algorithms. As
innovative aspect, evaluation results are shown and analysed as disjoint groups of stereo correspondence
algorithms with comparable accuracy. This innovation is obtained by a partitioning and grouping algorithm.
On the other hand, the used error measure offers advantages over the error measure used in the
Middlebury’s methodology. The experimental validation is based on the Middlebury’s test-bed and
algorithms repository. The obtained results show seven groups with different accuracies. Moreover, the topranked
stereo correspondence algorithms by the Middlebury’s methodology are not necessarily the most
accurate in the proposed methodology.
Face Recognition for Different Facial Expressions Using Principal Component a...AM Publications
The face is our primary focus of attention in social intercourse, playing a major role in conveying
identity and emotion. We can recognize thousands of faces learned throughout our lifetime and identify familiar faces
at a glance even after years of separation. This skill is quite robust, despite large changes in the visual stimulus due to
viewing conditions, expression, aging, and distractions such as glasses, beards, changes in hairstyle. Though human
faces are complex in shape, face recognition is not difficult for a human brain whereas for a computer this job is not
easy. In this paper presents and analyzes the performance of Principle Component Analysis (PCA) based technique for
face recognition. We consider recognition of human faces with two facial expressions: single and differential. The
images that are captured previously constitute the training set. From these images eigenfaces are calculated. The image
that is going to be recognized through our system is mapped to the same eigenspaces. Next I used classification
technique namely distance based used to classify the images as recognized or non-recognized. Presently I got result for
the single facial expression now I am working for different facial expression.
Multiple features based fingerprint identification systemeSAT Journals
Abstract Security has become major issue now a day. In order to prevent unauthorized access of confidential data there is a need for accurate and reliable personal identification system. So, biometric based identification system is one of the best solutions. Fingerprint based system is one of oldest biometric identification systems. It is used in many commercial and security applications. Even with advent of technology in fingerprint identification system, the accurate extraction and matching of features from a fingerprint image is a challenging task. The task is much more challenging when fingerprint is affected by non-linear deformations such as rotation and translation. In this paper, fingerprint identification system using improved feature vector based algorithm is presented. In the algorithm Gabor filter is implemented to enhance the fingerprint image. The salient features minutiae (ridge endings) and reference point are extracted from the image. The Euclidian distances between reference point and each minutiae point are calculated and are arranged in ascending order. These are stored in database as feature vectors. The fingerprint matching is done based on the similarity rate between the feature vector of input fingerprint and the feature vectors stored in the database. Algorithms are implemented using Visual Studio 2010 in C++ language using Open CV libraries and tested on the fingerprint database created in the laboratory. Key Words: Fingerprint, Minutiae, Reference point, Euclidian distance, Similarity rate, Identification
A HYBRID COPY-MOVE FORGERY DETECTION TECHNIQUE USING REGIONAL SIMILARITY INDICESijcsit
Different methods have been experimented for processing and detecting forgery in digital images. Image forgery involves various activities like copy-move forgery, image slicing, retouching, morphing etc. In copy-move forgery a portion within the image is copied and pasted on another part of the same image,generally to conceal or enhance certain portions of the image. This paper proposes a copy-move forgery detection using local fractal dimension and structural similarity indices. The image is classified into different texture regions based on the local fractal dimension. Forgery checking is thus confined to be among the portions within a region. Structural similarity index measure is applied to each block pair in each region to localize the forged portion. Experimental results prove that this hybrid method can effectively detect such kind of image tampering with minimum false positives.
Visual Cryptography for biometric privacywaseem ahmad
visual cryptography for biometric privacy Preserving the privacy of digital biometric data (e.g., face images) stored in a central database has become of paramount importance. This work explores the possibility of using visual cryptography for imparting privacy to biometric data
Authentication of a person is the major concern in this era for security purposes. In biometric systems Signature is one of the behavioural features used for the authentication purpose. In this paper we work on the offline signature collected through different persons. Morphological operations are applied on these signature images with Hough transform to determine regular shape which assists in authentication process. The values extracted from this Hough space is used in the feed forward neural network which is trained using back-propagation algorithm. After the different training stages efficiency found above more than 95%. Application of this system will be in the security concerned fields, in the defence security, biometric authentication, as biometric computer protection or as method of the analysis of person’s behaviour changes.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Documento con el reglamento para las elecciones del nuevo CAIM 2011-2012 y Consejero Superior. Este documento será aprobado/rechazado el día 28 de noviembre y se ruega que se lea.
A evolução do aparato normativo de proteção à fauna diante dos atos de caça n...Franco Nassaro
Este artigo analisa primeiramente aspectos gerais da prática de caça e do extrativismo animal no Brasil e, na sua segunda parte, apresenta a evolução da legislação de proteção à fauna no país tendo por referência inicial a década de 1930, com base no estudo das normas sistematizadas em cinco fases (até 1934, de 1934 a 1967, de 1967 a 1988, de 1988 a 1998, após 1998). No período ocorreram expressivas mudanças do ordenamento jurídico, sobrevindo legislação restritiva aos atos de caça. As circunstâncias em que surgiram normas específicas tendo por objeto a relação entre os homens e os animais silvestres revelam uma dinâmica própria e caracterizam momentos distintos, porém interligados em um mesmo processo. Essas normas guardam vínculo com a questão da caça associada ao aproveitamento dos recursos faunísticos e com a resposta do poder público objetivando o controle do extrativismo animal.
Publicado na revista Tempos Históricos, da Universidade do Oeste do Estado do Paraná (UNIOESTE), em 2011, conforme informações no rodapé. Publicação impressa e digital.
COMPRESSION BASED FACE RECOGNITION USING DWT AND SVMsipij
The biometric is used to identify a person effectively and employ in almost all applications of day to day
activities. In this paper, we propose compression based face recognition using Discrete Wavelet Transform
(DWT) and Support Vector Machine (SVM). The novel concept of converting many images of single person
into one image using averaging technique is introduced to reduce execution time and memory. The DWT is
applied on averaged face image to obtain approximation (LL) and detailed bands. The LL band coefficients
are given as input to SVM to obtain Support vectors (SV’s). The LL coefficients of DWT and SV’s are fused
based on arithmetic addition to extract final features. The Euclidean Distance (ED) is used to compare test
image features with database image features to compute performance parameters. It is observed that, the
proposed algorithm is better in terms of performance compared to existing algorithms.
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.
J.K.Jeevitha ,B.Karthika,E.Devipriya "Face Recognition using LDN Code", International Research Journal of Engineering and Technology (IRJET), Volume2,issue-01 April 2015.e-ISSN:2395-0056, p-ISSN:2395-0072. www.irjet.net
Abstract
LDN characterizes both the texture and contrast information of facial components in a compact way, producing a more discriminative code than other available methods. An LDN code is obtained by computing the edge response values in 8 directions at each pixel with the aid of a compass mask. Image analysis and understanding has recently received significant attention, especially during the past several years. At least two reasons can be accounted for this trend: the first is the wide range of commercial and law enforcement applications, and the second is the availability of feasible technologies after nearly 30 years of research. In this paper we propose a novel local feature descriptor, called Local Directional Number Pattern (LDN), for face analysis, i.e., face and expression recognition. LDN characterizes both the texture and contrast information of facial components in a compact way, producing a more discriminative code than other available methods.
Human face detection is a significant problem of
image processing and is usually a first step for face
recognition and visual surveillance. This paper presents the
details of face detection approach that is implemented to
achieve accurate face detection in group color images which
are based on facial feature and Support Vector Machine. In
the first step, the proposed approach quickly separates skin
color regions from the background and from non-skin color
regions using YCbCr color space transformation. After the
detection of skin regions, the images are processed with,
wavelet transforms (WT) and discrete cosine transforms
(DCT) as a result of which the 30×30 pixel sub images are
found. These sub images are then assigned to SVM classifier
as an input. The SVM is used to classify non-face regions from
the remaining regions more accurately, that are obtained
from previous steps and having big difference between faces
regions and non-faces regions. The experimental results on
different types of group color images show that this approach
improves the detection speed and minimizes the false
detection rate in less time and detects faces in different color
images.
Shadow Detection and Removal Techniques A Perspective Viewijtsrd
Shadow detection and removal from single images of natural scenes is the main problem. Hence, Shadow detection and removal remained a challenging task. Significant research carried out on different shadow detection techniques. Over the last decades several approaches were introduced to deal with shadow detection and removal. Shadows are visual phenomena which happen when an area in the scene is occluded from the primary light source e.g. sun . Shadows are everywhere around us and we are rarely Confused by their presence. This article provides an overview of various methods used for shadow detection and removal using some main components like texture analysis, color information, Gaussian mixture model GMM and deterministic non model based approach. Texture Color This paper is aimed to provide a survey on various algorithms and methods of shadow detection and removal with their comparative study. Avinash Kumar Singh | Ankit Pandit ""Shadow Detection and Removal Techniques: A Perspective View"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25201.pdf
Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/25201/shadow-detection-and-removal-techniques-a-perspective-view/avinash-kumar-singh
ABSTRACT: a rigorous work on static and dynamic appearance based classification systems for face is on but, it is proving to be a challenging task for researchers to design a proper system since human face is complex one. Decades of work was and is focussed on how to classify a face and on how to increase the rate of classification but, little attention was paid to overcome redundancy in image classification. This paper presents a novel idea which focuses on redundancy check and its elimination. The paper after drawing inferences from previous work gives out a novel idea for exact face classification and elimination of redundancy.
COMPRESSION BASED FACE RECOGNITION USING TRANSFORM DOMAIN FEATURES FUSED AT M...sipij
The physiological biometric trait face images are used to identify a person effectively. In this paper, we
propose compression based face recognition using transform domain features fused at matching level. The
2D images are converted into 1-D vectors using mean to compress number of pixels. The Fast Fourier
Transform (FFT) and Discrete Wavelet Transform (DWT) are used to extract features. The low and high
frequency coefficients of DWT are concatenated to obtained final DWT features. The performance
parameters are computed by comparing database and test image features of FFT and DWT using Euclidian
Distance (ED). The performance parameters of FFT and DWT are fused at matching level to obtain better
results. It is observed that the performance of proposed method is better than the existing methods.
Deep learning for pose-invariant face detection in unconstrained environmentIJECEIAES
In the recent past, convolutional neural networks (CNNs) have seen resurgence and have performed extremely well on vision tasks. Visually the model resembles a series of layers each of which is processed by a function to form a next layer. It is argued that CNN first models the low level features such as edges and joints and then expresses higher level features as a composition of these low level features. The aim of this paper is to detect multi-view faces using deep convolutional neural network (DCNN). Implementation, detection and retrieval of faces will be obtained with the help of direct visual matching technology. Further, the probabilistic measure of the similarity of the face images will be done using Bayesian analysis. Experiment detects faces with ±90 degree out of plane rotations. Fine tuned AlexNet is used to detect pose invariant faces. For this work, we extracted examples of training from AFLW (Annotated Facial Landmarks in the Wild) dataset that involve 21K images with 24K annotations of the face.
Abstract: Face Recognition appears to be an integral part in human-computer interfaces and eservices. To carry out security and fault tolerance various Image Processing techniques have been incorporated using ‘Curse of Dimensionality’ that refers to Classifying a pattern with high dimensions that requires a large number of training data. A face recognition & Detection system is a computer-driven application for automatically identifying or verifying a person from still or video image. It does that by comparing selected facial features in the live image and a facial database where the system returns a possible list of faces corresponding to training samples from the database. The nodal points are measured creating a numerical code, called a faceprint, representing the face in the database. Relatively many techniques are used. Image processing technique has been implemented using Feature extraction by Gabor Filters and database training data using Neural Networks. Multiscale resolution and matrix sampling is efficiently performed using this technique.
Keywords: Image Processing techniques, Curse of Dimensionality, Faceprint, Feature extraction, Gabor Filters, Neural Networks.
Title: Face Recognition & Detection Using Image Processing
Author: Chandani Sharma
International Journal of Recent Research in Mathematics Computer Science and Information Technology (IJRRMCSIT)
Paper Publications
LITERATURE SURVEY ON SPARSE REPRESENTATION FOR NEURAL NETWORK BASED FACE DETE...csijjournal
Face detection and recognition is a challenging problem in the field of image processing. In this paper, we reviewed some of the recent research works on face recognition. Issues with the previous face recognition
techniques are , time required is more for face recognition , recognition rate and database required to store the data . To overcome these problems sparse representation based classifier technique can be used.
Humans are able to process a face in a variety of
ways to categorize it by its identity, along with a number of
other demographic characteristics, including race, gender ,
and age. Experimental results are based on a face database
containing subjects. Race and gender also play an important
role in face-related applications. Experimental results are
indicated that participants categorized the race of the face
and this categorization drives the perceptual process. A face
image data set is collected from Internet, and divided into a
training dataset and a test dataset. Experimental results based
on a face database containing 250 subjects. The proposed
system can also be applied to other image-based classification
tasks.
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.
Independent Component Analysis of Edge Information for Face RecognitionCSCJournals
In this paper we address the problem of face recognition using edge information as independent components. The edge information is obtained by using Laplacian of Gaussian (LoG) and Canny edge detection methods then preprocessing is done by using Principle Component analysis (PCA) before applying the Independent Component Analysis (ICA) algorithm for training of images. The independent components obtained by ICA algorithm are used as feature vectors for classification. The Euclidean distance and Mahalanobis distance classifiers are used for testing of images. The algorithm is tested on two different databases of face images for variation in illumination and facial poses up to 180 degree rotation angle.
Independent Component Analysis of Edge Information for Face Recognition
Bb32351355
1. Einstein.J, DivyaBaskaran / International Journal of Engineering Research and Applications
(IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 2, March -April 2013, pp.351-355
Face-Name Graph Matching For The Personalities In Movie
Screen
Einstein.J*, DivyaBaskaran**
*(Asst. Professor, Dept. of IT, VelTech HighTech Dr. Rangarajan Dr.Sakunthala Engineering College,
Chennai.)
** (Final Year Student, M.Tech IT, Vel Tech Dr. RR &Dr. SR Technical University, Chennai.)
Abstract
In image processing, various biometric computer vision research community over past two
applications, name identification from facial decades.
images plays an important role, Weber’s Local There are 2 main steps involved in
Descriptor (WLD) will be used for face recognizing names of humans presented in an image
recognition for name identification.WLD is a .These are face detection and name classification
texture descriptor that performs better than ,which are applied consecutively. In order to exploit
other similar descriptors but it is holistic due to uniqueness of faces in name recognition, the first
its very construction. We divide an image into a step is to detect and localize those faces in the
number of blocks, calculate WLD descriptor for images. This is the task achieved by face detection
each block and concatenate them. This spatial systems.
WLD descriptor has better discriminatory As face detection is one of popular research
power. It is used to represent the image in terms areas, many algorithms have been proposed for it.
of differential excitations and gradient Most of them are based on the same idea
orientation histogram for texture analysis.WLD considering the face detection as a binary
is based on Weber’s law and it is robust to classification task. That is, given a part of image, the
illumination change, noise and other distortions. task is to decide whether it is a face or not .This is
So it effectively analyzes the face features to achieved by first transforming the given region into
accurate matching and name identification. The features and then using classifier trained on example
feature extraction approach will be used for both images to decide if these features represent a human
test and database images to recognize for name face. faces can appear in various locations and can
identification. The face will be recognized by also show themselves in various sizes, often a
finding Euclidean distance between them. The window-sliding technique is also employed .The
proposed spatial WLD descriptor with simplest idea is to have the classifier classifying the portions
classifier gives much better accuracy with lesser of an image, at all location and scales, as face or
algorithmic complexity than face recognition non-face.
approaches.
2. RELATED WORKS
Keywords- Normalization, Orientation, This section discuss about the literature
Differential excitation, Euclidean Distance. survey done on various issues are
Cast list discovery problem: In the “cast list
1. INTRODUCTION discovery “problem the faces are clustered by
There are number of applications where appearance and faces of a particular character are
face recognition can play an important role expected to be collected in a few pure clusters.
including biometric authentication, high technology Names for the clusters are then manually selected
surveillance and security systems image retrieval from the cast list. Ramanan et al. Proposed to
and passive demographical data collections .it is manually label an initial set of face clusters and
observable that our behaviour and social interaction further cluster the rest face instances based on
are face recognition system could have great impact clothing within scenes the authors have addressed
in improving human computer interaction systems the problem of finding particular characters by
in such a way as to make them be more user- building a model/classifier of the character’s
friendly and acting more human-like. It is appearance from user-provided training data.
unarguable that face is one the most important Multiple learning problems: Multiple instances
feature that characterizes human beings. By only learning (MIL) is proposed for problems with
looking ones faces, we are not only able to tell who incomplete knowledge on data labels. Instead of
they are but also perceive a lot of information such receiving labelled instances as a conventional
as their emotions, ages and names. This is why face supervised method does, a MIL method receives a
recognition by face has received much interest in set of labelled bags, where each bag contains a
number of unlabelled instances. Thus ,a label can be
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2. Einstein.J, DivyaBaskaran / International Journal of Engineering Research and Applications
(IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 2, March -April 2013, pp.351-355
regarded as a constraint in the form of logical-or
relationship with the labels of the instances in the
bag.
Retrieval problem (AFR) in video: Recent years
have seen a development of algorithms that use
AFR for the analysis of media content. Most of
these deal with the retrieval problem in video
.Arandjelovi’c and zisserman use signature images,
obtained by a cascade of transformations of the
detected faces. These are matched using a robust
distance measure in an image to -image or image –
to-set fashion to retrieve film shots based on the
Figure 1: Block diagram for identifying face and
presence of specific actors. sivic et al. Match face
name
sets, representing individual faces using SIFT
descriptors corresponding to salient facial features.
Everingham and Zisserman employ a quasi3D 3.2WLD
model of the head to correct for varying pose and In this part, we describe the two
components of WLD. Differential excitation ((ξ)
enrich the training corpus via shot tracking.
and orientation (θ).After that we present how to
compute a WLD histogram for an input image (or
3. PROPOSED SYSTEM image region).
The paper proposes a simple, yet very
powerful and robust local descriptor, called the
Weber’s Local Descriptor (WLD).It is based on the
fact that human perception of a pattern depends not
only on the change of a stimulus (such as sound.
lightening)but also on the original intensity of the
stimulus specifically.WLD consists of two
components differential excitation component is a
function of the ratio between two terms. One is the
relative intensity differences of a current pixel
against its neighbours. The other is the intensity of Fig 2.WLD applied Image
the current pixel. For a given image, we use the two
components to construct a concatenated WLD 3.2.1 DIFFERENTIAL EXCITATION
histogram .Experimental results on the Brodatz and We use the intensity differences between
KTH-TIPS2-a texture databases show that WLD its neighbours and a current pixel as the changes of
impressively outperforms the other widely used the current pixel. By this means, we hope to find the
descriptors (e.g. ,Gabor and SIFT).In addition, salient variations within an image to simulate the
experimental results on human face detection also pattern perception of human beings. Specifically ,a
show a promising performance comparable to the differential excitationξ (xc) of a current pixel xc is
best known results on the MIT+CMU frontal face computed. We first calculate the differences
test set, the AR face dataset and the CMU profile between its neighbours and the centre point using
test set. the filter f00
3.1CLASSIFICATION
With all necessary features have been extracted, Where xi(i=0,1...p-1)denotes the i-th neighbours of
the final task is to decide whether or not those xc and p is the number of neighbours. Following
features represent female or male face .As there are hints in Weber’s Law, we then compute the ratio of
obviously two decisions to make this essentially the differences to the intensity of the current point
binary classification task, that is the classifier is by combining the outputs of the two filters f00 and
trained on the female and male example face images f01(whose output 01 s v is the original image in
so that it learns the decision boundary between these fact).
two classes. After that it uses what it learn to make a
decision on the given face images.
We then employ the arctangent function on
Gratio(٠):
Combining (2), (3) and (4), we have:
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3. Einstein.J, DivyaBaskaran / International Journal of Engineering Research and Applications
(IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 2, March -April 2013, pp.351-355
the weber’s fraction. Weber’s Law, more simply
stated, says that the size of a just noticeable
difference(i.e., ΔI) is a constant proportion of the
So, the differential excitation of the current pixel original stimulus value. So ,for example, in a noisy
ξ(xc) is computed as: environment one must shout to be heard while a
whisper works in a quiet room.
Note that ξ(x) may take a minus value if the
neighbour intensities are smaller than that of the
current pixel. By this means, we attempt to preserve
more discriminating information in comparison to
using the absolute value of ξ(x).Intuitively, if ξ(x) is
positive; it simulates the case that the surroundings
are lighter than the current pixel. In contrast, if ξ(x)
is negative, it simulates the case that the
surroundings are darker than the current pixel.
3.2.2 ORIENTATION
The orientation component of WLD is the
gradient orientation as in, which is computed as
Where 10 s v and 11 s v are the outputs of the filters
f10 and f11
Fig 3: Illustration of computation of the WLD
For simplicity, θ is further quantized into T
Descriptor.
dominant orientations. Before the quantization, we
perform the mapping f :θaθ ′ :
4. IMPLEMENTATION
After faces are detected by face detection
algorithm, they need to be decided his or her names
.This is the task achieved by name identification
based on face recognition. Similar to the face
detection task, the name identification task is also
considered as a binary classification problem and it
will be done by recognizing the faces to identify the
Whereθ∈ [-π/2, π/2] and θ ′ ∈ [0, 2π]. This mapping name though the database. Essentially, Name
considers the value of θ and the sign of 10 s v and identification through face recognition consists of 4
11 s v. The quantization functions is then as follows main steps (1)Pre-processing, (2) Feature
Detection,(3) Euclidean Distance and(4) Name
Classification.
4.1PRE-PROCESSING
3.3WEBER’S LAW Since, in real-life, it is unlikely that people
Ernst Weber, an experimental psychologist will face directly and frontally towards the camera,
in the 19th century, observed that the ratio of the face images often consist of some in-plane and out-
increment threshold to the background intensity is a of-plane Rotations. Moreover, it is also unlikely that
constant. This relationship, known since as weber’s the light condition will be the same for all images.
Law, can be expressed as These variations greatly affect an accuracy of name
classifiers. The purpose of pre-processing step is
thus to remove these variations as much as possible.
As with other computer vision applications, there is
Where ΔI represents the increment no unique solution to this problem. The common
threshold(just noticeable difference for techniques involved in pre-processing step are face
discrimination).I represents the initial stimulus alignment, and light normalization. Face alignment
intensity and K signifies that the proportion on the tries to align faces such that they are closed to a
left side of the equation remains constant despite common or specified pose of face as much as
variations in the term. The fraction ΔI/I is known as
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4. Einstein.J, DivyaBaskaran / International Journal of Engineering Research and Applications
(IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 2, March -April 2013, pp.351-355
possible, whereas light normalization tries to get rid 5. CONCLUSION
of the variation in illumination. one of the common The proposed schemes are useful to
employed normalization techniques in the name improve results for identification of the face tracks
classification field is histogram equalization. extracted from uncontrolled movie videos from the
sensitivity analysis. It is shown that to some degree
4.1.1NORMALIZATION such schemes have better robustness to the noises in
Normalization is a process that changes the constructing affinity graphs than the traditional
range of pixel intensity values. The linear methods. It gives best results in noise full
normalization of a grey scale digital image is environment also. Future scenario will extend the
performed according to the formula work to investigate that the optimal functions for
different movie genres.
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*Author: Einstein.J, received B.Tech degree in
Information Technology from Anna University in
2005, M.Tech degree from Anna University in
2008.His current research interest include
Cryptography, Image processing, Computer
networks, Cloud computing etc. He is a Asst.
Professor in the Department of Information
Technology in VelTech HighTech Dr. Rangarajan &
Dr. Sakunthala Engineering College, Avadi,
Chennai, India. Einstein.J has published Eight
international publications and presented Six research
papers in international and national conferences,
having 4 years of teaching experience in various
institutions in India.
**Author: Divya Baskaran received B.Tech degree
in Information Technology from Periyar
Maniammai University, Thanjavur, India in
2011,Currrently pursuing M.Tech degree in
Information Technology from VelTech Dr . RR &
Dr. SR Technical University, Avadi, Chennai, India.
Divya Baskaran has published one international
publication.
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