Facial recognition using composite correlation filters designed with multiobjective combinatorial optimization
Andres Cuevas, Victor Hugo Diaz-Ramirez, Vitaly Kober, Leonardo Trujillo · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2014
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.