Face Recognition at-a-Distance Using Texture, Dense- and Sparse-Stereo Reconstruction

This paper introduces a framework for long-distance face recognition using dense and sparse stereo reconstruction, with texture of the facial region. Two methods to determine correspondences of the stereo pair are used in this paper: (a) dense global stereo-matching using maximum-a-posteriori Markov Random Fields (MAP-MRF) algorithms and (b) Active Appearance Model (AAM) fitting of both images of the stereo pair and using the fitted AAM mesh as the sparse correspondences. Experiments are performed using combinations of different features extracted from the dense and sparse reconstructions, as well as facial texture. The cumulative rank curves (CMC), which are generated using the proposed framework, confirms the feasibility of the proposed work for long distance recognition of human faces.