A novel facial expression recognition based on the curevlet features

Curvelet transform has been recently proved to be a powerful tool for multi-resolution analysis on images. In this paper we propose a new approach for facial expression recognition based on features extracted via curvelet transform. First curvelet transform is presented and its advantages in image analysis are described. Then the coefficients of curvelet in selected scales and angles are used as features for image analysis. Consequently the Principal Component Analysis (PCA) and Linear Discriminate Analysis (LDA) are used to reduce and optimize the curvelet features. Finally we use the nearest neighbor classifier to recognize the facial expressions based on these features. The experimental results on JAFFE and Cohn Kanade two benchmark databases show that the proposed approach outperforms the PCA and LDA techniques on the original image pixel values as well as its counterparts with the wavelet features

Performance Analysis of Face Recognition using Feed Forward Neural Network and PCA

In this paper, a face recognition system for personal identification and verification using Principal Component Analysis (PCA) with Neural Networks (NN) is proposed. The dimensionality of face images is reduced by the PCA and the recognition is done by the NN for efficient and robust face recognition. In this also focuses on the face database with different sources of variations, especially pose, expression. In this method of face identification covariance matrix of training and testing samples is prepared, which is further utilized for finding the eigenvalues and eigenvectors. These components are utilized for training of the face identification model. The algorithm has been tested on 165 grayscale images (15*11 classes). Face will be categorized as known or unknown face after matching with the present YALE face database. Experimental results in this paper showed that an accuracy of 96.4% was achieved

Selection and Combination of Local Gabor Classifiers for Robust Face Verification

Gabor features have been extensively used for facial image analysis due to their powerful representation capabilities. This paper focuses on selecting and combining multiple Gabor classifiers that are trained on, for example, different scales and local regions. The system exploits curvature Gabor features in addition to conventional Gabor features. Final classifier is obtained by combining selected classifiers using Sequential Forward Floating Search-based selection mechanism. In addition, we combine classifiers trained on different local representations at score-level by learning he weights with partial least square regression. The system is evaluated on Face Recognition Grand Challenge (FRGC) version 2.0 Experiment 4. The proposed system achieves 94.16% verification rate @ 0.1% FAR, which is the highest accuracy reported on this experiment so far in the literature

Face Recognition Using Curvelet and Waveatom Transform

The field of digital image processing is continually evolving. Nowadays, there is a significant increase in the level of interest in image morphology, neural networks, full-color image processing, image data compression and image recognition. This work deals with image recognition with the application of face recognition. Some people think that face recognition is an easy task for computer system as for humans, but in reality most of the face recognition systems can’t achieve a complete reliable performance because there are many factors affect on the process of recognition like: large variations in facial approach, head size and orientation, and change in environmental conditions, all these factors makes face recognition one of the fundamental problems in pattern analysis, other factors that impact the performance are the accuracy of face location stage and the number of actual face recognition techniques used in each system. So face recognition from still and video images is emerging as an active research area with numerous commercial and law enforcement application. This research identifies two techniques for face features extraction based on two different multiresolution analysis tools; the first called Curvelet transform while the second is waveatom transform. The resultant features are inputted to train via two famous classifiers; one of them is the artificial neural network (ANN) and the other is hidden Markov model (HMM). Experiments are carried out on two well-known datasets; AT&T dataset consists of 400 images corresponding to 40 people, and Essex Grimace dataset consists of 360 images corresponding to 18 people. Experimental results show the strength of both curvelets and waveatom features. On one hand, waveatom features obtained the highest accuracy rate of 99% and 100% with HMM classifier, and 98% and 100% with ANN classifier, for AT&T and Essex Grimace datasets, respectively. On the other hand, two levels Curvelet features achieved accuracy rate of 98% and 100% with HMM classifier, and 97% and 100% with ANN classifier, for AT&T and Essex Grimace datasets, respectively. A comparative study for waveatom with wavelet-based, curvelet-based, and traditional Principal Component Analysis (PCA) techniques is also presented. The proposed techniques supersede all of them. And shows the robustness of feature extraction methods used against included and occluded effects. Also, indicates the potential of HMM over ANN, as they are classifiers

The efficacy of the Eigenvector approach to South African sign language identification

Masters of ScienceThe communication barriers between deaf and hearing society mean that interaction between these communities is kept to a minimum. The South African Sign Language research group, Integration of Signed and Verbal Communication: South African Sign Language Recognition and Animation (SASL), at the University of the Western Cape aims to create technologies to bridge the communication gap. In this thesis we address the subject of whole hand gesture recognition. We demonstrate a method to identify South African Sign Language classifiers using an eigenvector ap- proach. The classifiers researched within this thesis are based on those outlined by the Thibologa Sign Language Institute for SASL. Gesture recognition is achieved in real- time. Utilising a pre-processing method for image registration we are able to increase the recognition rates for the eigenvector approach.South Afric

Biometric Applications Based on Multiresolution Analysis Tools

This dissertation is dedicated to the development of new algorithms for biometric applications based on multiresolution analysis tools. Biometric is a unique, measurable characteristic of a human being that can be used to automatically recognize an individual or verify an individual\u27s identity. Biometrics can measure physiological, behavioral, physical and chemical characteristics of an individual. Physiological characteristics are based on measurements derived from direct measurement of a part of human body, such as, face, fingerprint, iris, retina etc. We focussed our investigations to fingerprint and face recognition since these two biometric modalities are used in conjunction to obtain reliable identification by various border security and law enforcement agencies. We developed an efficient and robust human face recognition algorithm for potential law enforcement applications. A generic fingerprint compression algorithm based on state of the art multiresolution analysis tool to speed up data archiving and recognition was also proposed. Finally, we put forth a new fingerprint matching algorithm by generating an efficient set of fingerprint features to minimize false matches and improve identification accuracy. Face recognition algorithms were proposed based on curvelet transform using kernel based principal component analysis and bidirectional two-dimensional principal component analysis and numerous experiments were performed using popular human face databases. Significant improvements in recognition accuracy were achieved and the proposed methods drastically outperformed conventional face recognition systems that employed linear one-dimensional principal component analysis. Compression schemes based on wave atoms decomposition were proposed and major improvements in peak signal to noise ratio were obtained in comparison to Federal Bureau of Investigation\u27s wavelet scalar quantization scheme. Improved performance was more pronounced and distinct at higher compression ratios. Finally, a fingerprint matching algorithm based on wave atoms decomposition, bidirectional two dimensional principal component analysis and extreme learning machine was proposed and noteworthy improvements in accuracy were realized

Identification de personnes par fusion de différentes modalités biométriques

This thesis contributes to the resolution of the problems which are related to the analysis of the biometric data outcome from the iris, the fingerprint and the fusion of these two modalities, for person identification. Thus, after the evaluation of those proposed biometric systems, we have shown that the multimodal biometric system based on iris and fingerprint outperforms both monomodal biometric systems based whatsoever on the iris or on the fingerprint.Cette thèse contribue essentiellement à la résolution des problèmes liés à l'analyse des données biométriques issues de l'iris, de l'empreinte digitale et de la fusion de ces deux modalités pour l'identification de personne. Ainsi, après l'évaluation des trois systèmes biométriques proposés, nous avons prouvé que le système biométrique multimodal basé sur l'iris et l'empreinte digitale est plus performant que les deux systèmes biométriques monomodaux basés que se soit sur l'iris ou sur l'empreinte digitale

Biometric face recognition using multilinear projection and artificial intelligence

PhD ThesisNumerous problems of automatic facial recognition in the linear and multilinear subspace learning have been addressed; nevertheless, many difficulties remain. This work focuses on two key problems for automatic facial recognition and feature extraction: object representation and high dimensionality. To address these problems, a bidirectional two-dimensional neighborhood preserving projection (B2DNPP) approach for human facial recognition has been developed. Compared with 2DNPP, the proposed method operates on 2-D facial images and performs reductions on the directions of both rows and columns of images. Furthermore, it has the ability to reveal variations between these directions. To further improve the performance of the B2DNPP method, a new B2DNPP based on the curvelet decomposition of human facial images is introduced. The curvelet multi- resolution tool enhances the edges representation and other singularities along curves, and thus improves directional features. In this method, an extreme learning machine (ELM) classifier is used which significantly improves classification rate. The proposed C-B2DNPP method decreases error rate from 5.9% to 3.5%, from 3.7% to 2.0% and from 19.7% to 14.2% using ORL, AR, and FERET databases compared with 2DNPP. Therefore, it achieves decreases in error rate more than 40%, 45%, and 27% respectively with the ORL, AR, and FERET databases. Facial images have particular natural structures in the form of two-, three-, or even higher-order tensors. Therefore, a novel method of supervised and unsupervised multilinear neighborhood preserving projection (MNPP) is proposed for face recognition. This allows the natural representation of multidimensional images 2-D, 3-D or higher-order tensors and extracts useful information directly from tensotial data rather than from matrices or vectors. As opposed to a B2DNPP which derives only two subspaces, in the MNPP method multiple interrelated subspaces are obtained over different tensor directions, so that the subspaces are learned iteratively by unfolding the tensor along the different directions. The performance of the MNPP has performed in terms of the two modes of facial recognition biometrics systems of identification and verification. The proposed supervised MNPP method achieved decrease over 50.8%, 75.6%, and 44.6% in error rate using ORL, AR, and FERET databases respectively, compared with 2DNPP. Therefore, the results demonstrate that the MNPP approach obtains the best overall performance in various learning scenarios