Relevance-Weighted (2D)2LDA Image Projection Technique for Face Recognition

ETRI Journal

关键词: face recognition;    (2D)2LDA;    2-directional 2DLDA;    2DLDA;    Linear discriminant analysis (LDA);   

Others  :  1185795
DOI  :  10.4218/etrij.09.0108.0667

【 摘 要 】

In this paper, a novel image projection technique for face recognition application is proposed which is based on linear discriminant analysis (LDA) combined with the relevance-weighted (RW) method. The projection is performed through 2-directional and 2-dimensional LDA, or (2D)²LDA, which simultaneously works in row and column directions to solve the small sample size problem. Moreover, a weighted discriminant hyperplane is used in the between-class scatter matrix, and an RW method is used in the within-class scatter matrix to weigh the information to resolve confusable data in these classes. This technique is called the relevance-weighted (2D)²LDA, or RW(2D)²LDA, which is used for a more accurate discriminant decision than that produced by the conventional LDA or 2DLDA. The proposed technique has been successfully tested on four face databases. Experimental results indicate that the proposed RW(2D)²LDA algorithm is more computationally efficient than the conventional algorithms because it has fewer features and faster times. It can also improve performance and has a maximum recognition rate of over 97%.

   

【 参考文献 】

• [1]M. Turk and A. Pentland, "Eigenfaces for Recognition," J. Cognitive Neuroscience, vol. 3, no. 1, 1991, pp. 71-86.
• [2]P.N. Belhumeur, J.P. Hespanha, and D.J. Kiregman, "Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, 1997, pp. 711-720.
• [3]L. Chen et al., "A New LDA-Based Face Recognition System Which Can Solve the Small Sample Size Problem," Pattern Recognition, vol. 33, no. 10, 2000, pp. 1713-1726.
• [4]H. Yu and J. Yang, "A Direct LDA Algorithm for High-Dimensional Data—with Application to Face Recognition," Pattern Recognition, vol. 34, no. 10, 2001, pp. 2067-2070.
• [5]A.M. Martinez and A.C. Kak, "PCA versus LDA," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 2, 2001, pp. 228-233.
• [6]H-C. Kim, D. Kim, and S.W. Bang, "Face Recognition Using LDA Mixture Model," Pattern Recognition Letters, vol. 24, no. 15, 2003, pp. 2815-2821.
• [7]M. Ordowski and G. Meyer, "Geometric Linear Discriminant Analysis for Pattern Recognition," Pattern Recognition Letters, vol. 37, no. 3, 2004, pp. 421-428.
• [8]X.-Y. Jing, D. Zhang, and Y.-F. Yao, "Improvements on the Linear Discriminant Technique with Application to Face Recognition," Pattern Recognition Letters, vol. 24, no. 15, 2003, pp. 2695-2701.
• [9]F. Song et al., "A Parameterized Direct LDA and Its Application to Face Recognition," Neurocomputing, vol. 24, 2003, pp. 2695-2701.
• [10]J. Yang et al., "Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 1, 2004, pp, 131-137.
• [11]J. Meng and W. Zhang, "Volume Measure in 2DPCA-Based Face Recognition," Pattern Recognition Letters, vol. 28, no. 10, 2007, pp. 1203-1208.
• [12]Y. Wen and P. Shi, "Image PCA: A New Approach for Face Recognition," Proc. IEEE Inter. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), vol. 1, 2007, pp. I-1241-1244.
• [13]H. Xiong, M.N.S. Swamy, and M.O. Ahmad, "Two-Dimensional FLD for Face Recognition," Pattern Recognition, vol. 38, no. 7, 2005, pp. 1121-1124.
• [14]J. Yang et al., "Two-Dimensional Discriminant Transform for Face Recognition," Pattern Recognition, vol. 38, no. 7, 2005, pp. 1125-1129.
• [15]M. Li and B. Yuan, "2D-LDA: A Statistical Linear Discriminant Analysis for Image Matrix," Pattern Recognition Letters, vol. 26, no. 5, 2005, pp. 527-532.
• [16]S. Yan et al., "Multilinear Discriminant Analysis for Face Recognition," IEEE Trans. Image Processing, vol. 16, 2007, pp. 212-220.
• [17]D.S. Guru and T.N. Vikram, "2D Pairwise FLD: A Robust Methodology for Face Recognition," IEEE Workshop on Automatic Identification Advanced Technologies, 2007, pp. 99-102.
• [18]D. Zhang and Z. Zhou, "(2D)2PCA: Two-Directional Two-Dimensional PCA for Efficient Face Representation and Recognition," Neurocomputing, vol. 69, 2005, pp. 224-231.
• [19]P. Nagabhushan, D.S. Guru, and B.H. Shekar, "(2D)2 FLD: An Efficient Approach for Appearance Based Object Recognition," Neurocomputing, vol. 69, 2006, pp. 934-940.
• [20]S. Noushath, G. Hemantha, and P. Shivakumara, "(2D)2LDA: An Efficient Approach for Face Recognition," Pattern Recognition, vol. 39, no. 7, 2006, pp. 1396-1400.
• [21]Y. Li, Y. Gao, and H. Erdogan, "Weighted Pairwise Scatter to Improve Linear Discriminant Analysis," Proc. 6th Inter. Conf. Spoken Language Processing (ICSLP), vol. 4, 2000, pp. 608-611.
• [22]R. Lotlikar and R. Kothari, "Fractional-Step Dimensionality Reduction," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 6, 2000, pp. 623-627.
• [23]M. Loog, R.P.W. Duin, and R. Haeb-Umbach, "Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 7, 2001, pp. 762-766.
• [24]B. Yu, L. Jin, and P. Chen, "A New LDA-Based Method for Face Recognition," Proc. IEEE Int. Conf. Pattern Recognition, vol. 1, 2002, pp. 168-171.
• [25]J. Lu, K.N. Plataniotis, and A.N. Venetsanopoulos, "Face Recognition Using LDA-Based Algorithms," IEEE Trans. Neural Networks, vol. 14, no. 1, 2003, pp. 195-200.
• [26]J.R. Price and T.F. Gee, "Face Recognition Using Direct, Weighted Linear Discriminant Analysis and Modular Subspaces," Pattern Recognition, vol. 38, no. 2, 2005, pp. 209-219.
• [27]H. Wang, S. Chen, and Z. Hu, "Image Recognition Using Weighted Two-Dimensional Maximum Margin Criterion," Proc. 3rd Int. Conf. Natural Computation (ICNC), vol. 1, 2007, pp. 582-586.
• [28]J. Wang, E. Sung, and W. Yau, "Weighting Multi-Block Two-Dimensional Linear Discriminant Features for Face Verification," Proc. 6th Int. Conf. on Information, Communications and Signal Processing (ICICS), 2007, pp. 1-5.
• [29]E.K. Tang et al., "Linear Dimensionality Reduction Using Relevance Weighted LDA," Pattern Recognition, vol. 38, no. 4, 2005, pp. 485-493.
• [30]Y. Liang et al., "Generalizing Relevance Weighted LDA," Pattern Recognition, vol. 38, no. 11, 2005, pp. 2217-2219.
• [31]K. Chougdali, M. Jedra, and N. Zahid, "Face Recognition Using Relevance Weighted LDA with QR Decomposition," J. Graphics, Vision and Image Processing (GVIP), vol. 6, no. 2, 2006, pp. 27-31.
• [32]D. Jarchi and R. Boostani, "A New Weighted LDA Method in Comparison to Some Versions of LDA," Proc. World Academy of Science, Engineering and Technology (WASET), vol. 18, 2006, pp. 233-238.
• [33]P.J. Phillips et al., "The FERET Evaluation Methodology for Face Recognition Algorithms," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 10, 2000, pp. 1090-1104.
• [34]The CMU Technical Report CMU-RI-TR-01-02, "The CMU Pose, Illumination, and Expression (PIE) Database of Human Faces."
• [35]F.S. Samaria and A.C. Harter, "Parameterisation of a Stochastic Model for Human Face Identification," Proc. 2nd IEEE Workshop on Applications of Computer Vision, 1994, pp. 138-142.
• [36]A.S. Georghiades, P.N. Belhumeur, and D.J. Kriegman, "From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 6, 2001, pp. 643-660.