Multi-Objective Particle Swarm Optimization-based Feature Selection for Face Recognition

The curse of dimensionality is a well-known problem in biometric applications (e.g., biometric passports). The downside of this problem is that both the accuracy and speed of the biometric authentication process are reduced. This paper sets forth a feature selection (FS) method based on speed-constrained multi-objective particle swarm optimization (SMPSO). The proposed approach aims to reduce the size of the biometric features through the minimization of the intra-class variations and the maximization of the inter-class variations. Experiments have been conducted using several datasets from University of California–Irvine (UCI) to confirm the efficiency of SMPSO-based FS against state-of-the-art multi-objective FS approaches, such as the multi-objective evolutionary algorithm based on decomposition (MOEA/D) and the second non-dominated sorting genetic algorithm (NSGA-II). When compared to NSGA-II and MOEA/D, SMPSO gained 6.01% and 6.11%, respectively, in average classification accuracy. Moreover, SMPSO achieved the best accuracy compared to MOGA, a modified version of NSGA-II. The experimental results obtained by using a YALE Face database validated the effectiveness of the proposed approach in reducing the size of the biometric features while allowing a good recognition accuracy. The classification performance was improved by 8.2% compared with the performance of the stateof-the-art approaches.

[1]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[2]  Asif Ekbal,et al.  Multiobjective optimization for classifier ensemble and feature selection: an application to named entity recognition , 2011, International Journal on Document Analysis and Recognition (IJDAR).

[3]  Mugurel Ionut Andreica,et al.  Algorithmic Solutions to Some Transportation Optimization Problems with Applications in the Metallurgical Industry , 2009, ArXiv.

[4]  Hasan Demirel,et al.  Application of NSGA-II to feature selection for facial expression recognition , 2011, Comput. Electr. Eng..

[5]  King-Sun Fu,et al.  IEEE Transactions on Pattern Analysis and Machine Intelligence Publication Information , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Antonio Martínez-Álvarez,et al.  Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps , 2014, Knowl. Based Syst..

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

[8]  Nima Jafari Navimipour,et al.  Service allocation in the cloud environments using multi-objective particle swarm optimization algorithm based on crowding distance , 2017, Swarm Evol. Comput..

[9]  P. Villar,et al.  A multiobjective genetic algorithm for feature selection and granularity learning in fuzzy-rule based classification systems , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[10]  Enrique Alba,et al.  SMPSO: A new PSO-based metaheuristic for multi-objective optimization , 2009, 2009 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making(MCDM).

[11]  Simona Dinu,et al.  Multi-objective Assembly Line Balancing Using Fuzzy Inertia-adaptive Particle Swarm Algorithm , 2015 .

[12]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[13]  Chee Peng Lim,et al.  A multi-objective evolutionary algorithm-based ensemble optimizer for feature selection and classification with neural network models , 2014, Neurocomputing.

[14]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Mengjie Zhang,et al.  Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach , 2013, IEEE Transactions on Cybernetics.

[16]  Carlos A. Coello Coello,et al.  Multi-Objective Particle Swarm Optimizers: An Experimental Comparison , 2009, EMO.

[17]  S. Kannan,et al.  MULTIMODAL BIOMETRIC AUTHENTICATION USING PARTICLE SWARM OPTIMIZATION ALGORITHM WITH FINGERPRINT AND IRIS , 2012 .

[18]  Jian Yang,et al.  KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Asif Ekbal,et al.  Feature Selection Using Multiobjective Optimization for Named Entity Recognition , 2010, 2010 20th International Conference on Pattern Recognition.

[20]  Peyman Akhavan,et al.  Solving the Problem of Flow Shop Scheduling by Neural Network Approach , 2010, NDT.

[21]  P. Siarry,et al.  Multiobjective Optimization: Principles and Case Studies , 2004 .

[22]  Hanqing Lu,et al.  Supervised kernel locality preserving projections for face recognition , 2005, Neurocomputing.

[23]  M. Hamdan.  On the Disruption-level of Polynomial Mutation for Evolutionary Multi-objective Optimisation Algorithms , 2010, Comput. Informatics.

[24]  Jiawei Han,et al.  Orthogonal Laplacianfaces for Face Recognition , 2006, IEEE Transactions on Image Processing.

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

[26]  ZhangYong,et al.  Multi-Objective Particle Swarm Optimization Approach for Cost-Based Feature Selection in Classification , 2017 .

[27]  Rabia Jafri,et al.  A Survey of Face Recognition Techniques , 2009, J. Inf. Process. Syst..

[28]  Punam Bedi,et al.  Multimodal Biometric Authentication using PSO based Watermarking , 2012 .

[29]  Haider Banka,et al.  Cancer microarray data feature selection using multi-objective binary particle swarm optimization algorithm , 2016, EXCLI journal.

[30]  Sanaa Ghouzali,et al.  Inter-communication classification for multi-view face recognition , 2014, Int. Arab J. Inf. Technol..

[31]  Fakhri Karray,et al.  Multi-objective Feature Selection with NSGA II , 2007, ICANNGA.

[32]  Swagatam Das,et al.  Simultaneous feature selection and weighting - An evolutionary multi-objective optimization approach , 2015, Pattern Recognit. Lett..

[33]  Souad Larabi Marie-Sainte.  Spatial Projection Pursuit based on Multiobjective optimization , 2015, 2015 6th International Conference on Information and Communication Systems (ICICS).