23 September 2014 Facial recognition using composite correlation filters designed with multiobjective combinatorial optimization
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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.
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Andres Cuevas, Andres Cuevas, Victor H. Diaz-Ramirez, Victor H. Diaz-Ramirez, Vitaly Kober, Vitaly Kober, Leonardo Trujillo, Leonardo Trujillo, } "Facial recognition using composite correlation filters designed with multiobjective combinatorial optimization", Proc. SPIE 9217, Applications of Digital Image Processing XXXVII, 921710 (23 September 2014); doi: 10.1117/12.2062348; https://doi.org/10.1117/12.2062348
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