Issue No. 03 - March (2013 vol. 35)
O. Ocegueda , Dept. of Comput. Sci., Univ. of Houston, Houston, TX, USA
Tianhong Fang , Dept. of Comput. Sci., Univ. of Houston, Houston, TX, USA
S. K. Shah , Dept. of Comput. Sci., Univ. of Houston, Houston, TX, USA
I. A. Kakadiaris , Dept. of Comput. Sci., Univ. of Houston, Houston, TX, USA
We present a Markov Random Field model for the analysis of lattices (e.g., images or 3D meshes) in terms of the discriminative information of their vertices. The proposed method provides a measure field that estimates the probability of each vertex being “discriminative” or “nondiscriminative” for a given classification task. To illustrate the applicability and generality of our framework, we use the estimated probabilities as feature scoring to define compact signatures for three different classification tasks: 1) 3D Face Recognition, 2) 3D Facial Expression Recognition, and 3) Ethnicity-based Subject Retrieval, obtaining very competitive results. The main contribution of this work lies in the development of a novel framework for feature selection in scenaria in which the most discriminative information is smoothly distributed along a lattice.
Face, Three dimensional displays, Vectors, Face recognition, Image segmentation, Geometry, Algorithm design and analysis, face and gesture recognition, Feature evaluation and selection, object recognition, Markov random fields, segmentation, image processing and computer vision, pattern recognition
S. K. Shah, I. A. Kakadiaris, O. Ocegueda and Tianhong Fang, "3D Face Discriminant Analysis Using Gauss-Markov Posterior Marginals," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 35, no. , pp. 728-739, 2013.