Abstract
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.
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© 2009 Springer-Verlag Berlin Heidelberg
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Kanan, H.R., Moin, M.S. (2009). Recognizing Partially Occluded Faces from a Single Exemplar Image Per Person. In: Park, J.H., Chen, HH., Atiquzzaman, M., Lee, C., Kim, Th., Yeo, SS. (eds) Advances in Information Security and Assurance. ISA 2009. Lecture Notes in Computer Science, vol 5576. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02617-1_11
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DOI: https://doi.org/10.1007/978-3-642-02617-1_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02616-4
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