This document presents a face recognition algorithm based on Fourier Mellin Transform. It begins with an introduction to face recognition and challenges of illumination and pose variations. It then describes extracting illumination invariant features by computing depth maps from input images using a shape from shading algorithm. Fourier Mellin Transform is applied to the depth maps to extract features. Experiments on the ORL database showed the approach achieved 100% recognition with 4 training images and 95.7% recognition with 3 training images, demonstrating robustness to illumination and pose variations.
Face Recognition based on STWT and DTCWT using two dimensional Q-shift Filters IJERA Editor
The Biometrics is used to recognize a person effectively compared to traditional methods of identification. In this paper, we propose a Face recognition based on Single Tree Wavelet Transform (STWT) and Dual Tree Complex Wavelet transform (DTCWT). The Face Images are preprocessed to enhance quality of the image and resize. DTCWT and STWT are applied on face images to extract features. The Euclidian distance is used to compare features of database image with test face images to compute performance parameters. The performance of STWT is compared with DTCWT. It is observed that the DTCWT gives better results compared to STWT technique.
HVDLP : HORIZONTAL VERTICAL DIAGONAL LOCAL PATTERN BASED FACE RECOGNITION sipij
Face image is an efficient biometric trait to recognize human beings without expecting any co-operation from a person. In this paper, we propose HVDLP: Horizontal Vertical Diagonal Local Pattern based face recognition using Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP). The face images of different sizes are converted into uniform size of 108×990and color images are converted to gray scale images in pre-processing. The Discrete Wavelet Transform (DWT) is applied on pre-processed images and LL band is obtained with the size of 54×45. The Novel concept of HVDLP is introduced in the proposed method to enhance the performance. The HVDLP is applied on 9×9 sub matrix of LL band to consider HVDLP coefficients. The local Binary Pattern (LBP) is applied on HVDLP of LL band. The final features are generated by using Guided filters on HVDLP and LBP matrices. The Euclidean Distance (ED) is used to compare final features of face database and test images to compute the performance parameters.
A FACE RECOGNITION USING LINEAR-DIAGONAL BINARY GRAPH PATTERN FEATURE EXTRACT...ijfcstjournal
Face recognition is one the most interesting topic in the field in computer vision and image processing.
Face recognition is a processing system that recognizes and identifies individuals human by their faces.
Automatic face recognition is powerful way to provide, authorized access to control their system. Face
recognition has many challenging problems (like face pose, face expression variation, illumination
variation, face orientation and noise) in the field of image analysis and computer vision. This method is
work on feature extraction part of face recognition. New way to extract face feature using LD-BGP code
operator it is like LGS and LBP feature extraction operator. In our LD-BGP-code operator work in two
direction first linear then diagonal. In both direction, its create eight digits code to every pixel of image.
Means of these two directional are taken so that is cover all neighbor of center pixel. First linear direction,
only horizontal and vertical pixel are taken. Second diagonal direction only diagonal pixels taken. In
matching phase, we use Euclidean distance to match a face image. We perform the Linear and diagonal
directional operator method on face database ORL. We get accuracy 95.3 %. LD-BGP method also works
on different type image like illuminated and expression variation image.
Face skin color based recognition using local spectral and gray scale featureseSAT Journals
Abstract Human face conveys more information about identity of person. Human face recognition is one of the most challenging problem and it can be used in many applications at different security places in airports, defense and banking sectors etc.In this work used colored features obtained from color segmentation because in real time scenario color provides the more information than gray scale image but it has a drawback. To overcome this drawback gray scale feature extracted from co-occurrence matrix of an image and for efficient face recognition of human Face under different illumination conditions spectral features can be extracted from face texture. These three feature vectors concatenated into a single feature vector and applied Lenc-Kral matching technique to measure similarity between the database and query image, the similarity is high then face is recognized. Keywords: Face recognition, illumination condition, local texture features, color segmentation.
Face Recognition based on STWT and DTCWT using two dimensional Q-shift Filters IJERA Editor
The Biometrics is used to recognize a person effectively compared to traditional methods of identification. In this paper, we propose a Face recognition based on Single Tree Wavelet Transform (STWT) and Dual Tree Complex Wavelet transform (DTCWT). The Face Images are preprocessed to enhance quality of the image and resize. DTCWT and STWT are applied on face images to extract features. The Euclidian distance is used to compare features of database image with test face images to compute performance parameters. The performance of STWT is compared with DTCWT. It is observed that the DTCWT gives better results compared to STWT technique.
HVDLP : HORIZONTAL VERTICAL DIAGONAL LOCAL PATTERN BASED FACE RECOGNITION sipij
Face image is an efficient biometric trait to recognize human beings without expecting any co-operation from a person. In this paper, we propose HVDLP: Horizontal Vertical Diagonal Local Pattern based face recognition using Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP). The face images of different sizes are converted into uniform size of 108×990and color images are converted to gray scale images in pre-processing. The Discrete Wavelet Transform (DWT) is applied on pre-processed images and LL band is obtained with the size of 54×45. The Novel concept of HVDLP is introduced in the proposed method to enhance the performance. The HVDLP is applied on 9×9 sub matrix of LL band to consider HVDLP coefficients. The local Binary Pattern (LBP) is applied on HVDLP of LL band. The final features are generated by using Guided filters on HVDLP and LBP matrices. The Euclidean Distance (ED) is used to compare final features of face database and test images to compute the performance parameters.
A FACE RECOGNITION USING LINEAR-DIAGONAL BINARY GRAPH PATTERN FEATURE EXTRACT...ijfcstjournal
Face recognition is one the most interesting topic in the field in computer vision and image processing.
Face recognition is a processing system that recognizes and identifies individuals human by their faces.
Automatic face recognition is powerful way to provide, authorized access to control their system. Face
recognition has many challenging problems (like face pose, face expression variation, illumination
variation, face orientation and noise) in the field of image analysis and computer vision. This method is
work on feature extraction part of face recognition. New way to extract face feature using LD-BGP code
operator it is like LGS and LBP feature extraction operator. In our LD-BGP-code operator work in two
direction first linear then diagonal. In both direction, its create eight digits code to every pixel of image.
Means of these two directional are taken so that is cover all neighbor of center pixel. First linear direction,
only horizontal and vertical pixel are taken. Second diagonal direction only diagonal pixels taken. In
matching phase, we use Euclidean distance to match a face image. We perform the Linear and diagonal
directional operator method on face database ORL. We get accuracy 95.3 %. LD-BGP method also works
on different type image like illuminated and expression variation image.
Face skin color based recognition using local spectral and gray scale featureseSAT Journals
Abstract Human face conveys more information about identity of person. Human face recognition is one of the most challenging problem and it can be used in many applications at different security places in airports, defense and banking sectors etc.In this work used colored features obtained from color segmentation because in real time scenario color provides the more information than gray scale image but it has a drawback. To overcome this drawback gray scale feature extracted from co-occurrence matrix of an image and for efficient face recognition of human Face under different illumination conditions spectral features can be extracted from face texture. These three feature vectors concatenated into a single feature vector and applied Lenc-Kral matching technique to measure similarity between the database and query image, the similarity is high then face is recognized. Keywords: Face recognition, illumination condition, local texture features, color segmentation.
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.
This paper presents a new local facial feature descriptor, Local Gray Code Pattern (LGCP), for facial expression recognition in contrast to widely adopted Local Binary pattern. Local Gray Code Pattern (LGCP) characterizes both the texture and contrast information of facial components. The LGCP descriptor is obtained using local gray color intensity differences from a local 3x3 pixels area weighted by their corresponding TF (term frequency). I have used extended Cohn-Kanade expression (CK+) dataset and Japanese Female Facial Expression (JAFFE) dataset with a Multiclass Support Vector Machine (LIBSVM) to evaluate proposed method. The proposed method is performed on six and seven basic expression classes in both person dependent and independent environment. According to extensive experimental results with prototypic expressions on static images, proposed method has achieved the highest recognition rate, as compared to other existing appearance-based feature descriptors LPQ, LBP, LBPU2, LBPRI, and LBPRIU2.
Local directional number pattern for face analysis face and expression recogn...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
FPGA ARCHITECTURE FOR FACIAL-FEATURES AND COMPONENTS EXTRACTIONijcseit
Several methods for detecting the face and extracting the facial features and components exist in the
literature. These methods are different in their complexity, performance, type and nature of the images and
the targeted application. The facial features and components are used in security applications, robotics and
assistance for the disabled. We use these components and characteristics to determine the state of alertness
and fatigue for medical diagnoses. In this work we use plain color background images whose color is
different from the skin and which contain a single face. We are interested in FPGA implementation of this
application. This implementation must meet two constraints, which are the execution time and the FPGA
resources. We have selected and have associated a face detection algorithm based on the skin detection
(using the RGB space) with a facial-feature extraction algorithm based on tracking the gradient and the
geometric model.
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.
Unimodal Multi-Feature Fusion and one-dimensional Hidden Markov Models for Lo...IJECEIAES
The objective of low-resolution face recognition is to identify faces from small size or poor quality images with varying pose, illumination, expression, etc. In this work, we propose a robust low face recognition technique based on one-dimensional Hidden Markov Models. Features of each facial image are extracted using three steps: firstly, both Gabor filters and Histogram of Oriented Gradients (HOG) descriptor are calculated. Secondly, the size of these features is reduced using the Linear Discriminant Analysis (LDA) method in order to remove redundant information. Finally, the reduced features are combined using Canonical Correlation Analysis (CCA) method. Unlike existing techniques using HMMs, in which authors consider each state to represent one facial region (eyes, nose, mouth, etc), the proposed system employs 1D-HMMs without any prior knowledge about the localization of interest regions in the facial image. Performance of the proposed method will be measured using the AR database.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Human’s facial parts extraction to recognize facial expressionijitjournal
Real-time facial expression analysis is an important yet challenging task in human computer interaction.
This paper proposes a real-time person independent facial expression recognition system using a
geometrical feature-based approach. The face geometry is extracted using the modified active shape
model. Each part of the face geometry is effectively represented by the Census Transformation (CT) based
feature histogram. The facial expression is classified by the SVM classifier with exponential chi-square
weighted merging kernel. The proposed method was evaluated on the JAFFE database and in real-world
environment. The experimental results show that the approach yields a high recognition rate and is
applicable in real-time facial expression analysis.
In this paper, we present an automatic application of 3D face recognition system using geodesic distance in Riemannian geometry. We consider, in this approach, the three dimensional face images as residing in Riemannian manifold and we compute the geodesic distance using the Jacobi iterations as a solution of the Eikonal equation. The problem of solving the Eikonal equation, unstructured simplified meshes of 3D face surface, such as tetrahedral and triangles are important for accurately modeling material interfaces and curved domains, which are approximations to curved surfaces in R3. In the classifying steps, we use: Neural Networks (NN), K-Nearest Neighbor (KNN) and Support Vector Machines (SVM). To test this method and evaluate its performance, a simulation series of experiments were performed on 3D Shape REtrieval Contest 2008 database (SHREC2008).
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.
This paper presents a new local facial feature descriptor, Local Gray Code Pattern (LGCP), for facial expression recognition in contrast to widely adopted Local Binary pattern. Local Gray Code Pattern (LGCP) characterizes both the texture and contrast information of facial components. The LGCP descriptor is obtained using local gray color intensity differences from a local 3x3 pixels area weighted by their corresponding TF (term frequency). I have used extended Cohn-Kanade expression (CK+) dataset and Japanese Female Facial Expression (JAFFE) dataset with a Multiclass Support Vector Machine (LIBSVM) to evaluate proposed method. The proposed method is performed on six and seven basic expression classes in both person dependent and independent environment. According to extensive experimental results with prototypic expressions on static images, proposed method has achieved the highest recognition rate, as compared to other existing appearance-based feature descriptors LPQ, LBP, LBPU2, LBPRI, and LBPRIU2.
Local directional number pattern for face analysis face and expression recogn...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
FPGA ARCHITECTURE FOR FACIAL-FEATURES AND COMPONENTS EXTRACTIONijcseit
Several methods for detecting the face and extracting the facial features and components exist in the
literature. These methods are different in their complexity, performance, type and nature of the images and
the targeted application. The facial features and components are used in security applications, robotics and
assistance for the disabled. We use these components and characteristics to determine the state of alertness
and fatigue for medical diagnoses. In this work we use plain color background images whose color is
different from the skin and which contain a single face. We are interested in FPGA implementation of this
application. This implementation must meet two constraints, which are the execution time and the FPGA
resources. We have selected and have associated a face detection algorithm based on the skin detection
(using the RGB space) with a facial-feature extraction algorithm based on tracking the gradient and the
geometric model.
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.
Unimodal Multi-Feature Fusion and one-dimensional Hidden Markov Models for Lo...IJECEIAES
The objective of low-resolution face recognition is to identify faces from small size or poor quality images with varying pose, illumination, expression, etc. In this work, we propose a robust low face recognition technique based on one-dimensional Hidden Markov Models. Features of each facial image are extracted using three steps: firstly, both Gabor filters and Histogram of Oriented Gradients (HOG) descriptor are calculated. Secondly, the size of these features is reduced using the Linear Discriminant Analysis (LDA) method in order to remove redundant information. Finally, the reduced features are combined using Canonical Correlation Analysis (CCA) method. Unlike existing techniques using HMMs, in which authors consider each state to represent one facial region (eyes, nose, mouth, etc), the proposed system employs 1D-HMMs without any prior knowledge about the localization of interest regions in the facial image. Performance of the proposed method will be measured using the AR database.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Human’s facial parts extraction to recognize facial expressionijitjournal
Real-time facial expression analysis is an important yet challenging task in human computer interaction.
This paper proposes a real-time person independent facial expression recognition system using a
geometrical feature-based approach. The face geometry is extracted using the modified active shape
model. Each part of the face geometry is effectively represented by the Census Transformation (CT) based
feature histogram. The facial expression is classified by the SVM classifier with exponential chi-square
weighted merging kernel. The proposed method was evaluated on the JAFFE database and in real-world
environment. The experimental results show that the approach yields a high recognition rate and is
applicable in real-time facial expression analysis.
In this paper, we present an automatic application of 3D face recognition system using geodesic distance in Riemannian geometry. We consider, in this approach, the three dimensional face images as residing in Riemannian manifold and we compute the geodesic distance using the Jacobi iterations as a solution of the Eikonal equation. The problem of solving the Eikonal equation, unstructured simplified meshes of 3D face surface, such as tetrahedral and triangles are important for accurately modeling material interfaces and curved domains, which are approximations to curved surfaces in R3. In the classifying steps, we use: Neural Networks (NN), K-Nearest Neighbor (KNN) and Support Vector Machines (SVM). To test this method and evaluate its performance, a simulation series of experiments were performed on 3D Shape REtrieval Contest 2008 database (SHREC2008).
Most face recognition algorithms are generally capable to achieve a high level of accuracy when
the image is acquired under wellcontrolled conditions. The face should be still during the acquisition
process; otherwise, the resulted image would be blur and hard for recognition. Enforcing persons to stand
still during the process is impractical; extremely likely that recognition should be performed on a blurred
image. It is important to understand the relation between the image blur and the recognition accuracy. The
ORL Database was used in the study. All images were in PGM format of 92 × 112 pixels from forty
different persons, ten images per person. Those images were randomly divided into training and testing
datasets with 50-50 ratio. Singular value decomposition was used to extract the features. The images in
the testing datasets were artificially blurred to represent a linear motion, and recognition was performed.
The blurred images were also filtered using various methods. The accuracy levels of the recognition on the
basis of the blurred faces and filtered faces were compared. The performed numerical study suggests that
at its best, the image improvement processes are capable to improve the recognition accuracy level by
less than five percent.
EV-SIFT - An Extended Scale Invariant Face Recognition for Plastic Surgery Fa...IJECEIAES
This paper presents a new technique called Entropy based SIFT (EV-SIFT) for accurate face recognition after the plastic surgery. The corresponding feature extracts the key points and volume of the scale-space structure for which the information rate is determined. This provides least effect on uncertain variations in the face since the entropy is the higher order statistical feature. The corresponding EV-SIFT features are applied to the Support vector machine for classification. The normal SIFT feature extracts the key points based on the contrast of the image and the V- SIFT feature extracts the key points based on the volume of the structure. However, the EV- SIFT method provides both the contrast and volume information. Thus EV-SIFT provide better performance when compared with PCA, normal SIFT and VSIFT based feature extraction.
A study of techniques for facial detection and expression classificationIJCSES Journal
Automatic recognition of facial expressions is an important component for human-machine interfaces. It
has lot of attraction in research area since 1990's.Although humans recognize face without effort or
delay, recognition by a machine is still a challenge. Some of its challenges are highly dynamic in their
orientation, lightening, scale, facial expression and occlusion. Applications are in the fields like user
authentication, person identification, video surveillance, information security, data privacy etc. The
various approaches for facial recognition are categorized into two namely holistic based facial
recognition and feature based facial recognition. Holistic based treat the image data as one entity without
isolating different region in the face where as feature based methods identify certain points on the face
such as eyes, nose and mouth etc. In this paper, facial expression recognition is analyzed with various
methods of facial detection,facial feature extraction and classification.
An Efficient Face Recognition Using Multi-Kernel Based Scale Invariant Featur...CSCJournals
Face recognition has gained significant attention in research community due to its wide range of commercial and law enforcement applications. Due to the developments in the past few decades, in the current scenario, face recognition is employing advanced feature identification techniques and matching methods. In spite of vast research done, face recognition still remains an open problem due to the challenges posed by illumination, occlusions, pose variation, scaling, etc. This paper is aimed at proposing a face recognition technique with high accuracy. It focuses on face recognition based on improved SIFT algorithm. In the proposed approach, the face features are extracted using a novel multi-kernel function (MKF) based SIFT technique. The classification is done using SVM classifier. Experimental results shows the superiority of the proposed algorithm over the SIFT technique. Evaluation of the proposed approach is done on CVL face database and experimental results shows that the proposed approach has a recognition rate of 99%.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
Facial recognition (FR) is a pattern recognition problem, in which images can be considered as a matrix of
pixels.There are manychallenges that affect the performance of face recognitionincluding illumination
variation, occlusion, and blurring. In this paper,a few preprocessing techniques are suggested to handle the
illumination variationsproblem. Also, other phases of face recognition problems like feature extraction and
classification are discussed. Preprocessing techniques like Histogram Equalization (HE), Gamma Intensity
Correction (GIC), and Regional Histogram Equalization (RHE) are tested inthe AT&T database. For
feature extraction, methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis
(LDA), Independent Component Analysis (ICA), and Local Binary Pattern (LBP) are applied. Support
Vector Machine (SVM) is used as the classifier. Both holistic and block-based methods are tested using the
AT&T database. For twelve different combinations of preprocessing, feature extraction, and classification
methods, experiments involving various block sizes are conducted to assess the computation performance
and recognition accuracy for the AT&T dataset.Using the block-based method, 100% accuracy is achieved
with the combination of GIC preprocessing, LDA feature extraction,and SVM classification using 2x2
block-sizingwhile the holistic method yields the maximum accuracy of 93.5%. The block-sized algorithm
performs better than the holistic approach under poor lighting conditions.SVM Radial Basis Function
performs extremely well on theAT&Tdataset for both holistic and block-based approaches.
PERFORMANCE EVALUATION OF BLOCK-SIZED ALGORITHMS FOR MAJORITY VOTE IN FACIAL ...ijaia
Facial recognition (FR) is a pattern recognition problem, in which images can be considered as a matrix of
pixels.There are manychallenges that affect the performance of face recognitionincluding illumination
variation, occlusion, and blurring. In this paper,a few preprocessing techniques are suggested to handle the
illumination variationsproblem. Also, other phases of face recognition problems like feature extraction and
classification are discussed. Preprocessing techniques like Histogram Equalization (HE), Gamma Intensity
Correction (GIC), and Regional Histogram Equalization (RHE) are tested inthe AT&T database. For
feature extraction, methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis
(LDA), Independent Component Analysis (ICA), and Local Binary Pattern (LBP) are applied. Support
Vector Machine (SVM) is used as the classifier. Both holistic and block-based methods are tested using the
AT&T database. For twelve different combinations of preprocessing, feature extraction, and classification
methods, experiments involving various block sizes are conducted to assess the computation performance
and recognition accuracy for the AT&T dataset.Using the block-based method, 100% accuracy is achieved
with the combination of GIC preprocessing, LDA feature extraction,and SVM classification using 2x2
block-sizingwhile the holistic method yields the maximum accuracy of 93.5%. The block-sized algorithm
performs better than the holistic approach under poor lighting conditions.SVM Radial Basis Function
performs extremely well on theAT&Tdataset for both holistic and block-based approaches
PERFORMANCE EVALUATION OF BLOCK-SIZED ALGORITHMS FOR MAJORITY VOTE IN FACIAL ...gerogepatton
Facial recognition (FR) is a pattern recognition problem, in which images can be considered as a matrix of
pixels.There are manychallenges that affect the performance of face recognitionincluding illumination
variation, occlusion, and blurring. In this paper,a few preprocessing techniques are suggested to handle the
illumination variationsproblem. Also, other phases of face recognition problems like feature extraction and
classification are discussed. Preprocessing techniques like Histogram Equalization (HE), Gamma Intensity
Correction (GIC), and Regional Histogram Equalization (RHE) are tested inthe AT&T database. For
feature extraction, methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis
(LDA), Independent Component Analysis (ICA), and Local Binary Pattern (LBP) are applied. Support
Vector Machine (SVM) is used as the classifier. Both holistic and block-based methods are tested using the
AT&T database. For twelve different combinations of preprocessing, feature extraction, and classification
methods, experiments involving various block sizes are conducted to assess the computation performance
and recognition accuracy for the AT&T dataset.Using the block-based method, 100% accuracy is achieved
with the combination of GIC preprocessing, LDA feature extraction,and SVM classification using 2x2
block-sizingwhile the holistic method yields the maximum accuracy of 93.5%. The block-sized algorithm
performs better than the holistic approach under poor lighting conditions.SVM Radial Basis Function
performs extremely well on theAT&Tdataset for both holistic and block-based approaches.
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.
International Journal of Computer Science, Engineering and Information Techno...IJCSEIT Journal
Several methods for detecting the face and extracting the facial features and components exist in the
literature. These methods are different in their complexity, performance, type and nature of the images and
the targeted application. The facial features and components are used in security applications, robotics and
assistance for the disabled. We use these components and characteristics to determine the state of alertness
and fatigue for medical diagnoses. In this work we use plain color background images whose color is
different from the skin and which contain a single face. We are interested in FPGA implementation of this
application. This implementation must meet two constraints, which are the execution time and the FPGA
resources. We have selected and have associated a face detection algorithm based on the skin detection
(using the RGB space) with a facial-feature extraction algorithm based on tracking the gradient and the
geometric model.
SYMMETRICAL WEIGHTED SUBSPACE HOLISTIC APPROACH FOR EXPRESSION RECOGNITIONijcsit
Human face expression is one of the cognitive activity or attribute to deliver the opinions to others.This paper mainly delivers the performance of appearance based holistic approach subspace methods based on Principal Component Analysis (PCA). In this work texture features are extracted from face images using Gabor filter. It was observed that extracted texture feature vector space has higher dimensional and has
more number of redundant contents. Hence training, testing and classification time becomes more. The expression recognition accuracy rate is also reduced. To overcome this problem Symmetrical Weighted 2DPCA (SW2DPCA) subspace method is introduced. Extracted feature vector space is projected in to subspace by using SW2DPCA method. By implementing weighted principles on odd and even symmetrical
decomposition space of training samples sets proposed method have been formed. Conventional PCA and 2DPCA method yields less recognition rate due to larger variations in expressions and light due to more number of feature space redundant variants. Proposed SW2DPCA method optimizes this problem by reducing redundant contents and discarding unequal variants. In this work a well known JAFFE databases
is used for experiments and tested with proposed SW2DPCA algorithm. From the experimental results it was found that facial recognition accuracy rate of GF+SW2DPCA based feature fusion subspace method has been increased to 95.24% compared to 2DPCA method.
Happiness Expression Recognition at Different Age ConditionsEditor IJMTER
Recognition of different internal emotions of human face under various critical
conditions is a difficult task. Facial expression recognition with different age variations is
considered in this study. This paper emphasizes on recognition of facial expression like
happiness mood of nine persons using subspace methods. This paper mainly focuses on new
robust subspace method which is based on Proposed Euclidean Distance Score Level Fusion
(PEDSLF) using PCA, ICA, SVD methods. All these methods and new robust method is
tested with FGNET database. An automatic recognition of facial expressions is being carried
out. Comparative analysis results surpluses PEDSLF method is more accurate for happiness
emotional facial expression recognition.
3-D Face Recognition Using Improved 3D Mixed TransformCSCJournals
This paper deals with the using of Improved 3D Mixed Transform (3D-IMT) for face recognition problem. The mixed transform consists; Fourier based 3D radon transform plus 1-D Wavelet transform (which is also known as 3D Ridgelet transform). The Mixed Transform is improved by using Particle swarm optimization ( PSO) , the improvement involves the selection of the best of directions for smart rectangle-to-polar transform as a part of the 3D Radon Transformation. The 3D-IMT is applied to the 3D representation of face images, and yields a few number of features, these features is projected into the maximized projection that achieves good recognition rate using the Linear Discriminant Analysis (LDA).
A Novel Mathematical Based Method for Generating Virtual Samples from a Front...CSCJournals
This paper deals with one sample face recognition which is a new challenging problem in pattern recognition. In the proposed method, the frontal 2D face image of each person divided to some sub-regions. After computing the 3D shape of each sub-region, a fusion scheme is applied on sub-regions to create a total 3D shape for whole face image. Then, 2D face image is added to the corresponding 3D shape to construct 3D face image. Finally by rotating the 3D face image, virtual samples with different views are generated. Experimental results on ORL dataset using nearest neighbor as classifier reveal an improvement about 5% in recognition rate for one sample per person by enlarging training set using generated virtual samples. Compared with other related works, the proposed method has the following advantages: 1) only one single frontal face is required for face recognition and the outputs are virtual images with variant views for each individual 2) need only 3 key points of face (eyes and nose) 3) 3D shape estimation for generating virtual samples is fully automatic and faster than other 3D reconstruction approaches 4) it is fully mathematical with no training phase and the estimated 3D model is unique for each individual.
Similar to Fourier mellin transform based face recognition (20)
Tech transfer making it as a risk free approach in pharmaceutical and biotech iniaemedu
Tech transfer is a common methodology for transferring new products or an existing
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successfully brought out in the form of a product, there is no customer benefit, or satisfaction.
Moreover, high emphasis is in sustaining manufacturing with highest quality each and every time. It
is vital that tech transfer projects need to be executed flawlessly. To accomplish this goal, risk
management is crucial and project team needs to use the risk management approach seamlessly.