Feature Selection and Recognition of Face by using Hybrid Chaotic PSO-BFO and Appearance-Based Recognition Algorithms

Swarm intelligence based approaches are a recent optimization algorithm that simulates the groups collective behavior of decentralized and self-organized systems and have gained more proliferation due to a variety of applications and uses in the feature selection to solve the complex problems and classify the objects based on chosen optimal set of features. Feature selection is a process that selects a subset from the extracted features sets according to some criterions for optimization. In computer vision based face recognition systems, feature selection, and representation algorithms play an important role for the selection of optimal, and discriminatory sets of facial feature vectors from the face database. This paper presents a novel approach for facial feature selection by using Hybrid Particle Swarm Optimization PSO, and Bacterial Foraging Optimization BFO optimization algorithms. The hybrid approach consists of two parts: 1 two types of chaotic mappings are introduced in different phase of proposed hybrid algorithms which preserve the huge diversity of population and improve the global searching and exploration capability; 2 In proposed hybrid approach, appearance based holistic face representation and recognition approaches such as Principal Component Analysis PCA, Local Discriminant Analysis LDA, Independent Component Analysis ICA and Discrete Cosine Transform DCT extract feature vectors from the Yale face database. Then features are selected by applying hybrid Chaotic PSO and BFO algorithms for the selection of optimal set of features; it quickly searches the feature subspace of facial features that is the most beneficial for classification and recognition of individuals. From the experimental results, the authors have compared the performance of proposed hybrid approach with existing approaches and conclude that hybrid approach can be efficiently used for feature selection for classification and recognition of face of individuals.

[1]  Arun Ross,et al.  Handbook of Biometrics , 2007 .

[2]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[3]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[4]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[5]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[6]  Alex Pentland,et al.  View-based and modular eigenspaces for face recognition , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Marian Stewart Bartlett,et al.  Face recognition by independent component analysis , 2002, IEEE Trans. Neural Networks.

[8]  D. Karaboga,et al.  On the performance of artificial bee colony (ABC) algorithm , 2008, Appl. Soft Comput..

[9]  Ramin Rajabioun,et al.  Cuckoo Optimization Algorithm , 2011, Appl. Soft Comput..

[10]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[11]  Atta ur Rahman,et al.  Handbook of Research on Modeling, Analysis, and Application of Nature-Inspired Metaheuristic Algorithms , 2017 .

[12]  S. Wicker,et al.  Termite: ad-hoc networking with stigmergy , 2003, GLOBECOM '03. IEEE Global Telecommunications Conference (IEEE Cat. No.03CH37489).

[13]  Ming Yu,et al.  New Face Recognition Method Based on DWT/DCT Combined Feature Selection , 2006, 2006 International Conference on Machine Learning and Cybernetics.

[14]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

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

[16]  Caro Lucas,et al.  A novel numerical optimization algorithm inspired from weed colonization , 2006, Ecol. Informatics.

[17]  Ibrahim Eksin,et al.  A new optimization method: Big Bang-Big Crunch , 2006, Adv. Eng. Softw..

[18]  D. Pham,et al.  THE BEES ALGORITHM, A NOVEL TOOL FOR COMPLEX OPTIMISATION PROBLEMS , 2006 .

[19]  Q. H. Wu,et al.  Biologically inspired optimization: a review , 2009 .

[20]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[21]  Kevin M. Passino,et al.  Biomimicry of bacterial foraging for distributed optimization and control , 2002 .

[22]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[23]  W. Hsu,et al.  Handbook of Research on Computational Methodologies in Gene Regulatory Networks , 2009 .

[24]  Cihan Karakuzu.  Fuzzy controller training using particle swarm optimization for nonlinear system control. , 2008, ISA transactions.

[25]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

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

[28]  Abdolreza Hatamlou,et al.  Black hole: A new heuristic optimization approach for data clustering , 2013, Inf. Sci..

[29]  A.S. Samra,et al.  Face recognition using wavelet transform, fast Fourier transform and discrete cosine transform , 2003, 2003 46th Midwest Symposium on Circuits and Systems.

[30]  Cihan Karakuzu.  Retraction notice to: Fuzzy controller training using particle swarm optimization for nonlinear system control. , 2009, ISA transactions.

[31]  Sanjay Kumar Singh,et al.  Black Hole Algorithm and Its Applications , 2015, Computational Intelligence Applications in Modeling and Control.

[32]  Xin-She Yang,et al.  Nature-Inspired Metaheuristic Algorithms: Second Edition , 2010 .

[33]  Dong Hwa Kim,et al.  A hybrid genetic algorithm and bacterial foraging approach for global optimization , 2007, Inf. Sci..

[34]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[35]  Martin D. Levine,et al.  Face Recognition Using the Discrete Cosine Transform , 2001, International Journal of Computer Vision.

[36]  Ming-Huwi Horng,et al.  A multilevel image thresholding using the honey bee mating optimization , 2010, Appl. Math. Comput..

[37]  A.A. Kishk,et al.  Invasive Weed Optimization and its Features in Electromagnetics , 2010, IEEE Transactions on Antennas and Propagation.

[38]  B. Alatas,et al.  Chaos embedded particle swarm optimization algorithms , 2009 .

[39]  Kevin Hapeshi,et al.  A Review of Nature-Inspired Algorithms , 2010 .

[40]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[41]  Xin-She Yang,et al.  Bat algorithm: a novel approach for global engineering optimization , 2012, 1211.6663.

[42]  Ali Maroosi,et al.  Application of honey-bee mating optimization algorithm on clustering , 2007, Appl. Math. Comput..

[43]  Xin-She Yang,et al.  Multiobjective cuckoo search for design optimization , 2013, Comput. Oper. Res..

[44]  Hyeonjoon Moon,et al.  The FERET evaluation methodology for face-recognition algorithms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[45]  Xin-She Yang,et al.  Flower Pollination Algorithm for Global Optimization , 2012, UCNC.

[46]  Leonardo Vidal Batista,et al.  Face recognition using DCT coefficients selection , 2008, SAC '08.

[47]  K. Etemad,et al.  Discriminant analysis for recognition of human face images , 1997 .

[48]  Hamed Shah-Hosseini,et al.  The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm , 2009, Int. J. Bio Inspired Comput..

[49]  Chia-Feng Juang,et al.  Ant Colony Optimization Algorithm for Fuzzy Controller Design and Its FPGA Implementation , 2008, IEEE Transactions on Industrial Electronics.

[50]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[51]  Konstantinos N. Plataniotis,et al.  Face recognition using LDA-based algorithms , 2003, IEEE Trans. Neural Networks.

[52]  Sanghamitra Bandyopadhyay,et al.  Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients , 2007, Inf. Sci..