Face Recognition at-a-Distance using Texture, Dense- and Sparse-Stereo Reconstruction Ham M. Rara, Asem A. Ali, Shireen Y. Elhabian, Thomas L. Starr, Aly A. Farag University of Louisville {hmrara01,amali003,syelha01,tlstar01,aafara01}@louisville.edu Abstract This paper introduces a framework for long-distance face recognition using dense and sparse stereo re- construction, 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 Appear- ance Model (AAM) fitting of both images of the stereo pair and using the fitted AAM mesh as the sparse cor- respondences. Experiments are performed using com- binations of different features extracted from the dense and sparse reconstructions, as well as facial texture. The cumulative rank curves (CMC), which are gener- ated using the proposed framework, confirms the feasi- bility of the proposed work for long distance recognition of human faces. 1. Introduction Face recognition is a challenging task that has been an attractive research area in the past three decades [9]. The main theme of the solutions provided by different researchers involves detecting one or more faces from the given image, followed by facial feature extraction which can be used for recognition. Recently, there has been interest in face recogni- tion at-a-distance. Yao, et al. [8] created a face video database, acquired from long distances, high magnifica- tions, and both indoor and outdoor under uncontrolled surveillance conditions. They created a comprehensive processing algorithm to deal with image degradations related to long-distance image acquisition and were suc- cessful in improving recognition rates. Medioni, et al. [4] presented an approach to identify non-cooperative individuals at a distance by inferring 3D shape from a sequence of images. We constructed our own passive stereo acquisition setup and an accompanying database in [6]. Figure 1. Illustration of captured images: (a) 3-meter indoor (b) 15-meter indoor, (c) 30-meter outdoor, and (d) 50-meter out- door. In this paper, we used the same stereo setup to in- crease our database to a total of 61 subjects. In addition to previous indoor ranges, we acquired samples from outdoor ranges of 30 and 50 meters (Fig. 1). Exper- iments are then performed using various combinations of different features extracted from the dense and sparse reconstructions, as well as facial texture. The paper is organized as follows: Section 2 de- scribes stereo-based reconstruction, Section 3 discusses the features for recognition, Section 4 provides exper- imental results and related discussion, and Section 5 concludes the paper. 2. Stereo-matching Based Reconstruction Dense, Global Stereo Matching: The objective of the classical stereo problem is to find the pair of corre- sponding points p and q that result from the projection of the same scene point (X,Y,Z ) to the two images 2010 International Conference on Pattern Recognition 1051-4651/10 $26.00 © 2010 IEEE DOI 10.1109/ICPR.2010.304 1225 2010 International Conference on Pattern Recognition 1051-4651/10 $26.00 © 2010 IEEE DOI 10.1109/ICPR.2010.304 1225 2010 International Conference on Pattern Recognition 1051-4651/10 $26.00 © 2010 IEEE DOI 10.1109/ICPR.2010.304 1221 2010 International Conference on Pattern Recognition 1051-4651/10 $26.00 © 2010 IEEE DOI 10.1109/ICPR.2010.304 1221 2010 International Conference on Pattern Recognition 1051-4651/10 $26.00 © 2010 IEEE DOI 10.1109/ICPR.2010.304 1221