Optimization-based methodology for training set selection to synthesize composite correlation filters for face recognition

Face recognition has been addressed with pattern recognition techniques such as composite correlation filters. These filters are synthesized from training sets which are representative of facial classes. For this reason, the filter performance depends greatly on the appropriate selection of the training set. This set can be selected either by a filter designer or by a conventional method. This paper presents an optimization-based methodology for the automatic selection of the training set. Given an optimization algorithm, the proposed methodology uses its main mechanics to iteratively examine a given set of available images in order to find the best subset for the training set. To this end, three objective functions are proposed as optimization criteria for training set selection. The proposed methodology was evaluated by undertaking face recognition under variable illumination and facial expressions. Four optimization algorithms and three composite correlation filters were used to test the proposed methodology. The Maximum Average Correlation Height filter designed by Grey Wolf Optimizer obtained the best performance under homogeneous illumination and facial expressions, while the Unconstrained Nonlinear Composite Filter designed by either Grey Wolf Optimizer or (1+1)-Evolution Strategy obtained the best performance under variable illumination. The proposed methodology selects training sets for the synthesis of composite filters with competitive results comparable to the results reported in the face recognition literature.