3D Face Discriminant Analysis Using Gauss-Markov Posterior Marginals

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 35; no. 3; pp. 728 - 739
• Main Authors: Ocegueda, O., Tianhong Fang, Shah, S. K., Kakadiaris, I. A.
• Format: Journal Article
• Language: English
• Published: Los Alamitos, CA IEEE 01.03.2013; IEEE Computer Society
• Subjects: Algorithm design and analysis; Algorithms; Applied sciences; Artificial intelligence; Biometric Identification - methods; Computer science; control theory; systems; Data processing. List processing. Character string processing; Discriminant Analysis; Exact sciences and technology; Face; Face - anatomy & histology; face and gesture recognition; Face recognition; Feature evaluation and selection; Geometry; Humans; image processing and computer vision; Image segmentation; Imaging, Three-Dimensional - methods; Markov Chains; Markov random fields; Memory organisation. Data processing; object recognition; pattern recognition; Pattern recognition. Digital image processing. Computational geometry; segmentation; Software; Three dimensional displays; Vectors; Markov process; Computer vision; Discriminant analysis; Expressed emotion; Ethnic origin; Probabilistic approach; Segmentation; Image processing; Face recognition; Gesture recognition; Feature evaluation and selection; Markov model; Pattern recognition; Object recognition; image processing and computer vision; Lattice; Markov random fields; face and gesture recognition; Gaussian process; Classification; Facies; Selection criterion; Tridimensional image
• ISSN: 0162-8828; 1939-3539; 2160-9292
• DOI: 10.1109/TPAMI.2012.126

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.