Face recognition based on extreme learning machine

Abstract Extreme learning machine (ELM) is an efficient learning algorithm for generalized single hidden layer feedforward networks (SLFNs), which performs well in both regression and classification applications. It has recently been shown that from the optimization point of view ELM and support vector machine (SVM) are equivalent but ELM has less stringent optimization constraints. Due to the mild optimization constraints ELM can be easy of implementation and usually obtains better generalization performance. In this paper we study the performance of the one-against-all (OAA) and one-against-one (OAO) ELM for classification in multi-label face recognition applications. The performance is verified through four benchmarking face image data sets.

[1]  Xuelong Li,et al.  Geometric Mean for Subspace Selection , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  T. M. Williams,et al.  Practical Methods of Optimization. Vol. 1: Unconstrained Optimization , 1980 .

[3]  Rama Chellappa,et al.  Discriminant analysis of principal components for face recognition , 1998 .

[4]  Zhifeng Li,et al.  Using Support Vector Machines to Enhance the Performance of Bayesian Face Recognition , 2007, IEEE Transactions on Information Forensics and Security.

[5]  Yuan Lan,et al.  Extreme Learning Machine based bacterial protein subcellular localization prediction , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[6]  P. Jonathon Phillips,et al.  Support Vector Machines Applied to Face Recognition , 1998, NIPS.

[7]  Jiawei Han,et al.  SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis , 2008, IEEE Transactions on Knowledge and Data Engineering.

[8]  Guang-Bin Huang,et al.  Convex incremental extreme learning machine , 2007, Neurocomputing.

[9]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

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

[11]  Hongming Zhou,et al.  Optimization method based extreme learning machine for classification , 2010, Neurocomputing.

[12]  Tarek Helmy,et al.  Multi-category bioinformatics dataset classification using extreme learning machine , 2009, 2009 IEEE Congress on Evolutionary Computation.

[13]  Jiwen Lu,et al.  Palmprint recognition via Locality Preserving Projections and extreme learning machine neural network , 2008, 2008 9th International Conference on Signal Processing.

[14]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[15]  Amit Agarwal,et al.  A new machine learning paradigm for terrain reconstruction , 2006, IEEE Geoscience and Remote Sensing Letters.

[16]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[17]  Lei Chen,et al.  Enhanced random search based incremental extreme learning machine , 2008, Neurocomputing.

[18]  Tomaso A. Poggio,et al.  Face recognition: component-based versus global approaches , 2003, Comput. Vis. Image Underst..

[19]  Jun-Ying Gan,et al.  Face recognition based on 2DLDA and support vector machine , 2009, 2009 International Conference on Wavelet Analysis and Pattern Recognition.

[20]  D. B. Graham,et al.  Characterising Virtual Eigensignatures for General Purpose Face Recognition , 1998 .

[21]  Boonserm Kijsirikul,et al.  Multiclass support vector machines using adaptive directed acyclic graph , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[22]  Yoram Singer,et al.  Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers , 2000, J. Mach. Learn. Res..

[23]  Yew-Soon Ong,et al.  Extreme learning machine for multi-categories classification applications , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[24]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

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

[26]  Timothy F. Cootes,et al.  Active Appearance Models , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

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

[28]  Guodong Guo,et al.  Face recognition by support vector machines , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[29]  Shi-Yu Peng,et al.  Combination of dual-tree complex wavelet and SVM for face recognition , 2008, 2008 International Conference on Machine Learning and Cybernetics.

[30]  Chee Kheong Siew,et al.  Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes , 2006, IEEE Transactions on Neural Networks.

[31]  Dacheng Tao,et al.  Discriminative Locality Alignment , 2008, ECCV.

[32]  Sun-Yuan Kung,et al.  Face recognition/detection by probabilistic decision-based neural network , 1997, IEEE Trans. Neural Networks.

[33]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).