Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
An improved face recognition technique based on modular PCA approach
Pattern Recognition Letters
Journal of Cognitive Neuroscience
IEEE Transactions on Neural Networks
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Despite remarkable progress on human face recognition, little attention has been given to robustly recognizing partially occluded faces. In this paper, we propose a new approach to recognize partially occluded faces when only one exemplar image per person is available. In this approach, a face image is represented as an array of Patch PCA (PPCA) extracted from a partitioned face image containing information of local regions instead of holistic information of a face. An adaptive weighting technique is utilized to assign proper weights to PPCA features to adjust the contribution of each local region of a face in terms of the richness of identity information and the likelihood of occlusion in a local region. The encouraging experimental results using AR face database demonstrate that the proposed method provides a new solution to the problem of robustly recognizing partially occluded faces in single model databases.