Ensemble of multiresolution probabilistic neural network classifiers with fuzzy integral for face recognition

Probabilistic neural network (PNN) is simple and can be easily implemented. PNN has fast learning speed, and its outputs are posterior probabilities which facilitate the combination of classifiers with fuzzy integral. In this paper, we proposed a face recognition algorithm named EPNN, which combine PNN classifiers with fuzzy integral, and can make full use of the superiority of PNN and ensemble learning. The proposed method includes three stages: (1) the incomplete wavelet packet decomposition of face images; (2) training PNN classifiers with wavelet sub-images with low frequency components. (3) combination of the trained PNN classifiers by fuzzy integral. Compared with four matrix subspace algorithms, the proposed method can obtain competitive performance. Such as, it can improve the accuracy of face recognition with less CPU time. The experimental results on JAFFE, YALE, ORL and FERET confirm that the proposed method outperform the four matrix subspace algorithms.

[1]  Lawrence Sirovich,et al.  Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Harry Wechsler,et al.  The FERET database and evaluation procedure for face-recognition algorithms , 1998, Image Vis. Comput..

[3]  Gregory Shakhnarovich,et al.  Face Recognition in Subspaces , 2011, Handbook of Face Recognition.

[4]  Daoqiang Zhang,et al.  (2D)2PCA: Two-directional two-dimensional PCA for efficient face representation and recognition , 2005, Neurocomputing.

[5]  Jen-Tzung Chien,et al.  Discriminant Waveletfaces and Nearest Feature Classifiers for Face Recognition , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Yongzhao Zhan,et al.  A neural-AdaBoost based facial expression recognition system , 2014, Expert Syst. Appl..

[8]  Yulian Zhu,et al.  Semi-random subspace method for face recognition , 2009, Image Vis. Comput..

[9]  Ming Li,et al.  2D-LDA: A statistical linear discriminant analysis for image matrix , 2005, Pattern Recognit. Lett..

[10]  Bülent Sankur,et al.  ARTICLE IN PRESS Image and Vision Computing xx (2005) 1–9 www.elsevier.com/locate/imavis , 2004 .

[11]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[12]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[13]  L. Kuncheva,et al.  Combining classifiers: Soft computing solutions. , 2001 .

[14]  Andy Harter,et al.  Parameterisation of a stochastic model for human face identification , 1994, Proceedings of 1994 IEEE Workshop on Applications of Computer Vision.

[15]  Yuxiao Hu,et al.  Face recognition using Laplacianfaces , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Jian-Huang Lai,et al.  Discriminant subspace learning constrained by locally statistical uncorrelation for face recognition , 2013, Neural Networks.

[17]  Zhan Yong.  Fusion Recognition Algorithm Based on Fuzzy Density Determination with Classification Capability and Supportability , 2012 .

[18]  Michael J. Lyons,et al.  Automatic Classification of Single Facial Images , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Anil K. Jain,et al.  Handbook of Face Recognition, 2nd Edition , 2011 .

[20]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[21]  Alejandro F. Frangi,et al.  Two-dimensional PCA: a new approach to appearance-based face representation and recognition , 2004 .

[22]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[23]  Loris Nanni,et al.  An enhanced subspace method for face recognition , 2006, Pattern Recognit. Lett..

[24]  K. Kim,et al.  Face recognition using kernel principal component analysis , 2002, IEEE Signal Process. Lett..

[25]  Xiaogang Wang,et al.  A unified framework for subspace face recognition , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  John S. D. Mason,et al.  Adaptive classifier integration for robust pattern recognition , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[27]  Gregory T. Adams,et al.  The fuzzy integral , 1980 .

[28]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[29]  Ju-Chin Chen,et al.  Kernel discriminant transformation for image set-based face recognition , 2011, Pattern Recognit..

[30]  Kin-Man Lam,et al.  Multi-resolution feature fusion for face recognition , 2014, Pattern Recognit..

[31]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

[32]  Witold Pedrycz,et al.  Face recognition using fuzzy Integral and wavelet decomposition method , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[33]  David J. Kriegman,et al.  From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[34]  Palaiahnakote Shivakumara,et al.  (2D)2LDA: An efficient approach for face recognition , 2006, Pattern Recognit..

[35]  Gonzalo Pajares,et al.  A wavelet-based image fusion tutorial , 2004, Pattern Recognit..