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Volume 29 Issue 9
Jan.  2011
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Cheng TAO, Wenbo ZHENG, Yongzhi LI, Liu LIU. Achievable Rate Analysis on Massive MIMO-OFDM Systems with Low-resolution ADC[J]. Journal of Electronics & Information Technology, 2018, 40(10): 2294-2300. doi: 10.11999/JEIT180088
Citation: Zhuang Zhe-min, Zhang A-niu, Li Fen-lan. Based on an Optimized LDA Algorithm for Face Recognition[J]. Journal of Electronics & Information Technology, 2007, 29(9): 2047-2049. doi: 10.3724/SP.J.1146.2006.00319

Based on an Optimized LDA Algorithm for Face Recognition

doi: 10.3724/SP.J.1146.2006.00319
  • Received Date: 2006-03-20
  • Rev Recd Date: 2006-08-21
  • Publish Date: 2007-09-19
  • Extracting the most discriminant low-dimensional face feature is an extremely critical step in Face Recognition (FR) systems. Linear Discriminant Analysis (LDA) is one of the most popular linear classification techniques for feature extraction. An optimized LDA algorithm is introduced to overcome questions existing in the traditional LDA algorithm for FR in this paper. The between-class scatter matrix is redefined in order to make the traditional Fisher criterion optimal and eliminate the effect that the edge of class has on selecting the optimal projection; at the same time, it avoids computing the inverse of matrix by means of factorization, and solves the Small Sample Size (SSS) problem. Adopting experiential method, the appropriate value of e is found, and then the optimal effect of face recognition is got. Experimental results show the recognition rate of this method is superior to the traditional LDA.
  • Chellappa R, Wilson C, and Sirohey S. Human and machine recognition of faces: A survey[J].Proc. IEEE.1995, 83(5):705-741[2]Tplba A S, El-Baz A H, and El-Harby A A. Face recognition: A literature review. J. of Signal Processing, 2005, 2(1): 88-103.[3]Turk M and Pentland A. Eigenfaces for recognition[J].J. of Cognitive Neuroscience.1991, 3(1):71-86[4]Belhumeur P N, Hespanha J P, and Kriegman D J. Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Trans on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711-720.[5]Chen L F, Liao M, and Lin J C, et al.. A new LDA-based face recognition system which can solve the small samples size problem[J].J. of Pattern Recognition.2000, 33(10):1713-1726[6]Yu H and Yang J. Direct LDA algorithm for high dimensional data with application to face recognition[J].J. of Pattern Recognition.2001, 34(10):2067-2070[7]Huang R, Liu Q S, and Lu H Q, et al.. Solving the small sample size problem of LDA. IEEE Proceedings of the 16th International Conference on Pattern Recognition, Canada, Quebec 2002, 3: 29-32.[8]Lotlikar R and Kothari R. Fractional-step dimensionality reduction[J].IEEE Trans. on Pattern Analysis and Machine Intelligence.2000, 22(6):623-627[9]Loog M, Duin R P W, and Haeb-Umbach R. Multiclass linear dimension reduction by weighted pairwise fisher criteria[J].IEEE Trans. on Pattern Analysis and Machine Intelligence.2001, 23(7):762-766[10]Martinez A M and Zhu M. Where are linear feature extraction methods applicable? IEEE Trans[J].on Pattern Analysis and Machine Intelligence.2005, 27(1):1934-1944
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