Improving face recognition with multispectral fusion and support vector machines
Giovani Chiachia · Americanae (AECID Library) · 2009
Abstract—Face recognition is one of the primary ways of human identification. Although researches on automated face recognition have broadly increased along the last 35 years, it remains a challenging task in the fields of Computer Vision and Pattern Recognition. As the scenarios varies from static and constrained photographs to uncontrolled video images, the challenging issues on automatic face recognition are usually related with variations in illumination, pose and expressions. The goal of this master thesis is to propose techniques for the improvement of face recognition systems. The first technique addresses the problem of illumination by fusing the visible and the infrared spectra of the face in order to improve the recognition rates. The second technique addresses the issue of face features extraction and classification. It proposes a new framework for face recognition by using features extracted by Census Histograms and a pattern recognition technique based on Support Vector Machines (SVMs). The key contributions of this work are the statistical dependency analysis between face recognition systems based on different spectra and the applica-tion of a single C-SVC SVM to reliably predict faces identities. The obtained results indicate that the proposed techniques can contribute to improve automated face recognition rates.