Abstract
Faces are highly deformable objects which may easily change their appearance over time. Not all face areas are subject to the same variability. Therefore, decoupling of the information from independent areas of the face is of paramount importance to improve the robustness of any face recognition technique. The aim of this article is to present a robust face recognition technique based on the extraction and matching of probabilistic graphs drawn on scale invariant feature transform (SIFT) features related to independent face areas. The face matching strategy is based on matching individual salient facial graphs characterized by SIFT features as connected to facial landmarks such as the eyes and the mouth. In order to reduce the face matching errors, the Dempster–Shafer decision theory is applied to fuse the individual matching scores obtained from each pair of salient facial features. The proposed algorithm is evaluated with the Olivetti Research Lab (ORL) and the Indian Institute of Technology Kanpur (IITK) face databases. The experimental results demonstrate the effectiveness and potential of the proposed face recognition technique, even in the case of partially occluded faces.
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