Pixel selection based on discriminant features with application to face recognition
Pattern Recognition Letters
Robust sparse bounding sphere for 3D face recognition
Image and Vision Computing
Efficient 3D face recognition handling facial expression and hair occlusion
Image and Vision Computing
3D/4D facial expression analysis: An advanced annotated face model approach
Image and Vision Computing
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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 to be "discriminative" or "non-discriminative". As an application of the proposed framework, we present a method for the selection of compact and robust features for 3D face recognition. The resulting signature consists of 360 coefficients, based on which we are able to build a classifier yielding better recognition rates than currently reported in the literature. The main contribution of this work lies in the development of a novel framework for feature selection in scenarios in which the most discriminative information is known to be concentrated along piece-wise smooth regions of a lattice.