Feature selection for face recognition using DCT-PCA and Bat algorithm

Though the face recognition systems do not impose any constraints on users and also possess several advantages. Despite that, these still present some challenges such as facial expressions, sad, pose, illumination, age changes, and noise etc. There is a need to develop such method that copes with these challenges and yields better results. This paper makes use of DCT-PCA combination to reduce the dimensionality and extract the features followed by Bat algorithm to yield a set of features that proves to be the best for face recognition under uncontrolled environment. On comparison with other meta-heuristics such as GA, PSO, and CS, the results disclose that the proposed method outperforms others even in the existence of noise also.

[1]  Rafael S. Parpinelli,et al.  New inspirations in swarm intelligence: a survey , 2011, Int. J. Bio Inspired Comput..

[2]  Dinesh Kumar,et al.  Memetic Algorithms for Feature Selection in Face Recognition , 2008, 2008 Eighth International Conference on Hybrid Intelligent Systems.

[3]  N. Ahmed,et al.  Discrete Cosine Transform , 1996 .

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

[5]  V. K. Panchal,et al.  A novel approach based on nature inspired intelligence for face feature extraction and recognition , 2013, 2013 Sixth International Conference on Contemporary Computing (IC3).

[6]  Xin-She Yang,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010, NICSO.

[7]  Xin-She Yang,et al.  Bat algorithm: literature review and applications , 2013, Int. J. Bio Inspired Comput..

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

[9]  Zhong Yan,et al.  Ant Colony Optimization for Feature Selection in Face Recognition , 2004, ICBA.

[10]  Thomas Bäck,et al.  Evolutionary algorithms in theory and practice - evolution strategies, evolutionary programming, genetic algorithms , 1996 .

[11]  B.N. Araabi,et al.  Feature selection using genetic algorithm and it's application to face recognition , 2004, IEEE Conference on Cybernetics and Intelligent Systems, 2004..

[12]  Surekha Bhanot,et al.  Firefly inspired feature selection for face recognition , 2015, 2015 Eighth International Conference on Contemporary Computing (IC3).

[13]  Rama Chellappa,et al.  Human and machine recognition of faces: a survey , 1995, Proc. IEEE.

[14]  Rabab M. Ramadan,et al.  FACE RECOGNITION USING PARTICLE SWARM OPTIMIZATION-BASED SELECTED FEATURES , 2009 .

[15]  Manoj Kumar,et al.  Optimization of Feature Selection in Face Recognition System Using Differential Evolution and Genetic Algorithm , 2015, SocProS.

[16]  Caiyun Shi,et al.  The Application of Binary Particle Swarm Algorithm in Face Recognition , 2011, 2011 Seventh International Conference on Computational Intelligence and Security.