Topic
Eigenface
About: Eigenface is a research topic. Over the lifetime, 2128 publications have been published within this topic receiving 110119 citations.
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TL;DR: A new approach HLLR is proposed, based on conjunction of hybrid-eigenfaces and local linear regression LLR, to perform face recognition across pose, to generate virtual views in frontal and non-frontal poses.
Abstract: Pose variation leads to significant decline in the performance of the face recognition systems. In this paper, the authors propose a new approach HLLR, based on conjunction of hybrid-eigenfaces and local linear regression LLR, to perform face recognition across pose. In this approach, LLR on hybrid-eigenfaces is used to generate virtual views. These virtual views in frontal and non-frontal poses are obtained using frontal gallery image. The performance of the proposed approach is compared for classification accuracy with another efficient method based on global linear regression on hybrid eigenface HGLR. They also investigate the effect of number of images used to construct hybrid-eigenfaces on classification accuracy. Experimental results on two well known publicly available face databases demonstrate the effectiveness of the proposed approach. The suitability of proposed approach is also noticed when the number of available images is small.
7 citations
10 May 2010
TL;DR: Experimental results show that BPNN significantly improves the performance of face verification which is based on Euclidean distance and averages of improvement in equal error rate (EER) by range 62%–85% is achieved by BPNN.
Abstract: In this paper, we present back-propagation neural network (BPNN) as back-end classifier for face verification. Face features are extracted based on principal component analysis (PCA) and linear discriminant analysis (LDA). PCA efficiently reduces dimension of face images and represent them with eigenfaces; while LDA is alternatively used to improve discriminant ability of the PCA algorithm. Back-propagation neural network (BPNN) is used to learn the patterns of PCA and LDA features and produce relevant client and imposter scores for verification. The algorithms were evaluated using AT&T face database which comprises 40 subjects and with a total size of 400 images. Experimental results show that BPNN significantly improves the performance of face verification which is based on Euclidean distance. Percentages of improvement in equal error rate (EER) by range 62%–85% is achieved by BPNN.
7 citations
16 Sep 1996
TL;DR: The experiments show that the hybrid PCA/NN systems can improve the recognition rate by about 8% better than the PCA systems, on the authors' facial database, which contains large rotation face images as the testing sets.
Abstract: Principal component analysis (PCA) is a powerful statistical approach for extracting facial features for recognition. The eigenface method has been reported to provide significant recognition performance over various testing and evaluation procedures. We try to improve the PCA recognition performance by concatenating a probabilistic decision based neural networks (DBNN). Our experiments show that the hybrid PCA/NN systems can improve the recognition rate by about 8% better than the PCA systems, on our facial database, which contains large rotation face images as the testing sets.
7 citations
01 Jan 2001
TL;DR: Two Bayes plug-in covariance estimators known as RDA and LOOC are described and a new covariance estimate is proposed that does not require an optimisation procedure, but an eigenvector-eigenvalue ordering process to select information from the projected sample group covariance matrices whenever possible and the pooled covariance otherwise.
Abstract: Image pattern recognition problems, especially face and facial expression ones, are commonly related to “small sample size” problems In such applications there are a large number of features available but the number of training samples for each pattern is considerably less than the dimension of the feature space The Bayes plug-in classifier has been successfully applied to discriminate high dimensional data This classifier is based on similarity measures that involve the inverse of the sample group covariance matrices These matrices, however, are singular in “small sample size” problems Therefore, other methods of covariance estimation have been proposed where the sample group covariance estimate is replaced by covariance matrices of various forms In this paper, two Bayes plug-in covariance estimators known as RDA and LOOC are described and a new covariance estimator is proposed The new estimator does not require an optimisation procedure, but an eigenvector-eigenvalue ordering process to select information from the projected sample group covariance matrices whenever possible and the pooled covariance otherwise The effectiveness of the method is shown by experimental results carried out on face and facial expression recognition, using different databases for each application
7 citations
TL;DR: This research presents a system that is able to recognize a person’s face by comparing facial structure to that of a known person which is achieved by using frontal view facing photographs of individuals to render a two-dimensional representation of a human head.
Abstract: Face recognition is an important and challenging field in computer vision. This research present a system that is able to recognize a person’s face by comparing facial structure to that of a known person which is achieved by using frontal view facing photographs of individuals to render a two-dimensional representation of a human head. Various symmetrization techniques are used for preprocessing the image in order to handle bad illumination and face alignment problem. We used Eigenface approach for face recognition. Eigenfaces are eigenvectors of covariance matrix, representing given image space. Any new face image can then be represented as a linear combination of these Eigenfaces. This makes it easier to match any two given images and thus face recognition process. The implemented eigenface-based technique classified the faces 95% correctly.
7 citations