Facial recognition using composite correlation filters designed with multiobjective combinatorial optimization Andres Cuevas a , Victor H. Diaz-Ramirez a , Vitaly Kober b,c , and Leonardo Trujillo d a Instituto Polit´ ecnico Nacional - CITEDI, Ave. del Parque 1310, Mesa de Otay, Tijuana B.C. 22510, Mexico; b Department of Computer Science, CICESE, Carretera Ensenada-Tijuana 3918, Ensenada B.C. 22860, Mexico; c Department of Mathematics, Chelyabinsk State University, Russian Federation; d Instituto Tecnologico de Tijuana, Blvd. Industrial y Ave. ITR, Tijuana S/N, Mesa de Otay, Tijuana B.C. 22414, Mexico ABSTRACT Facial recognition is a difficult task due to variations in pose and facial expressions, as well as presence of noise and clutter in captured face images. In this work, we address facial recognition by means of composite correlation filters designed with multi-objective combinatorial optimization. Given a large set of available face images having variations in pose, gesticulations, and global illumination, a proposed algorithm synthesizes composite correlation filters by optimization of several performance criteria. The resultant filters are able to reliably detect and correctly classify face images of different subjects even when they are corrupted with additive noise and nonhomogeneous illumination. Computer simulation results obtained with the proposed approach are presented and discussed in terms of efficiency in face detection and reliability of facial classification. These results are also compared with those obtained with existing composite filters. Keywords: Facial recognition, composite correlation filters, multi-objective evolutionary algorithm, combina- torial optimization. 1. INTRODUCTION Over the last decades correlation filters have been successfully used for object detection and pattern classification problems. 1 A correlation filter is a linear system where the coordinates of the system’s output maximum are position estimates of the target within the scene. One advantage of correlation filters is that they perform well under noisy conditions. 2–4 Also, they possess a degradation-invariant property that tolerates partial occlusions of the target without significantly sacrificing their performance. 5 Correlation filters can be broadly classified in two main categories: analytical and composite. Analytical filters optimize a performance criterion from mathematical models of signals and noise. 6, 7 For facial recognition problems, these filters may not be recommended because the inherent variations in facial expression will rapidly degrade their recognition performance. On the other hand, composite filters are constructed by combination of several training templates, each of them representing an expected view of the target in the input image. 5 These filters have been successfully used for facial recognition. 1 Composite filters are synthesized by means of a supervised training process. Thus, the performance of the filters highly depend on a proper selection of templates used for training. 8 Commonly, training templates are chosen by a designer in an improvised manner. So, this subjective procedure is not optimal. In order to synthesize composite filters with improved performance for distortion tolerance facial recognition, we propose a filter design algorithm based on multi-objective combinatorial optimization. Given a large search space of training templates, the algorithm finds a subset of these templates that allows the synthesis of com- posite filters with an optimized performance in terms of several competitive criteria. The proposed algorithm combines two evolutionary strategies. Firstly, population management is performed by the Strength Pareto Evo- lutionary Algorithm 2 (SPEA2), 9 which is a state-of-the-art multi-objective evolutionary algorithm (MOEA). Secondly, individuals of the population are represented as variable length strings, while genetic operators applied to individuals are managed by the Speciation Adaptation Genetic Algorithm (SAGA). 10 The proposed design Applications of Digital Image Processing XXXVII, edited by Andrew G. Tescher, Proc. of SPIE Vol. 9217, 921710 © 2014 SPIE · CCC code: 0277-786X/14/$18 · doi: 10.1117/12.2062348 Proc. of SPIE Vol. 9217 921710-1 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 09/24/2014 Terms of Use: http://spiedl.org/terms