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Heteroscedastic multilinear discriminant analysis for face recognition
, Article Proceedings - International Conference on Pattern Recognition, 23 August 2010 through 26 August 2010, Istanbul ; 2010 , Pages 4287-4290 ; 10514651 (ISSN) ; 9780769541099 (ISBN) ; Manzuri Shalmani, M. T ; Sharif University of Technology
2010
Abstract
There is a growing attention in subspace learning using tensor-based approaches in high dimensional spaces. In this paper we first indicate that these methods suffer from the Heteroscedastic problem and then propose a new approach called Heteroscedastic Multilinear Discriminant Analysis (HMDA). Our method can solve this problem by utilizing the pairwise chernoff distance between every pair of clusters with the same index in different classes. We also show that our method is a general form of Multilinear Discriminant Analysis (MDA) approach. Experimental results on CMU-PIE, AR and AT&T face databases demonstrate that the proposed method always perform better than MDA in term of classification...
Extended two-dimensional PCA for efficient face representation and recognition
, Article 2008 IEEE 4th International Conference on Intelligent Computer Communication and Processing, ICCP 2008, Cluj-Napoca, 28 August 2008 through 30 August 2008 ; October , 2008 , Pages 295-298 ; 9781424426737 (ISBN) ; Manzuri Shalmani, M. T ; Khademi, M ; Sharif University of Technology
2008
Abstract
In this paper a novel method called Extended Two-Dimensional PCA (E2DPCA) is proposed which is an extension to the original 2DPCA. We state that the covariance matrix of 2DPCA is equivalent to the average of the main diagonal of the covariance matrix of PCA. This implies that 2DPCA eliminates some covariance information that can be useful for recognition. E2DPCA instead of just using the main diagonal considers a radius of r diagonals around it and expands the averaging so as to include the covariance information within those diagonals. The parameter r unifies PCA and 2DPCA. r=1 produces the covariance of 2DPCA, r=n that of PCA. Hence, by controlling r it is possible to control the...
Combination of wavelet and PCA for face recognition
, Article 2006 IEEE GCC Conference, GCC 2006, Manama, 20 March 2006 through 22 March 2006 ; 2006 ; 9780780395909 (ISBN) ; Kasaei, S ; Sharif University of Technology
2006
Abstract
This work presents a method to increase the face recognition accuracy using a combination of Wavelet, PCA, and Neural Networks. Preprocessing, feature extraction and classification rules are three crucial issues for face recognition. This paper presents a hybrid approach to employ these issues. For preprocessing and feature extraction steps, we apply a combination of wavelet transform and PCA. During the classification stage, the Neural Network (MLP) is explored to achieve a robust decision in presence of wide facial variations. The computational load of the proposed method is greatly reduced as comparing with the original PCA based method on the Yale and ORL face databases. Moreover, the...
Wavelet transform and fusion of linear and non linear method for face recognition
, Article DICTA 2009 - Digital Image Computing: Techniques and Applications, 1 December 2009 through 3 December 2009, Melbourne ; 2009 , Pages 296-302 ; 9780769538662 (ISBN) ; Kasaei, S ; Neissi, N. A ; Sharif University of Technology
Abstract
This work presents a method to increase the face recognition accuracy using a combination of Wavelet, PCA, KPCA, and RBF Neural Networks. Preprocessing, feature extraction and classification rules are three crucial issues for face recognition. This paper presents a hybrid approach to employ these issues. For preprocessing and feature extraction steps, we apply a combination of wavelet transform, PCA and KPCA. During the classification stage, the Neural Network (RBF) is explored to achieve a robust decision in presence of wide facial variations. At first derives a feature vector from a set of downsampled wavelet representation of face images, then the resulting PCA-based linear features and...
Construction and application of SVM model and wavelet-PCA for face recognition
, Article 2009 International Conference on Computer and Electrical Engineering, , 28 December 2009 through 30 December 2009, Dubai ; Volume 1 , 2009 , Pages 391-398 ; 9780769539256 (ISBN) ; Kasaei, S ; Alemi, H ; Sharif University of Technology
Abstract
This work presents a method to increase the face recognition accuracy using a combination of Wavelet, PCA, and SVM. Pre-processing, feature extraction and classification rules are three crucial issues for face recognition. This paper presents a hybrid approach to employ these issues. For pre-processing and feature extraction steps, we apply a combination of wavelet transform and PCA. During the classification stage, SVMs incorporated with a binary tree recognition strategy are applied to tackle the multi-class face recognition problem to achieve a robust decision in presence of wide facial variations. The binary trees extend naturally, the pairwise discrimination capability of the SVMs to...
A new incremental face recognition system
, Article 2007 4th IEEE Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS, Dortmund, 6 September 2007 through 8 September 2007 ; 2007 , Pages 335-340 ; 1424413486 (ISBN); 9781424413485 (ISBN) ; Ghavami, A ; Abrishami Moghaddam, H ; Sharif University of Technology
2007
Abstract
In this paper, we present new adaptive linear discriminant analysis (LDA) algorithm and apply them for adaptive facial feature extraction. Adaptive nature of the proposed algorithm is advantageous for real world applications in which one confronts with a sequence of data such as online face recognition and mobile robotics. Application of the new algorithm on feature extraction from facial image sequences is given in three steps: i) adaptive image preprocessing, ii) adaptive dimension reduction and iii) adaptive LDA feature estimation. Steps 1 and 2 are done simultaneously and outputs of stage 2 are used as a sequence of inputs for stage3. The proposed system was tested on Yale and PIE face...