Abstract
Automatically recognizing human faces with partial occlusions is one of the most challenging problems in face analysis community. This paper presents a novel string-based face recognition approach to address the partial occlusion problem in face recognition. In this approach, a new face representation, Stringface, is constructed to integrate the relational organization of intermediate-level features (line segments) into a high-level global structure (string). The matching of two faces is done by matching two Stringfaces through a string-to-string matching scheme, which is able to efficiently find the most discriminative local parts (substrings) for recognition without making any assumption on the distributions of the deformed facial regions. The proposed approach is compared against the state-of-the-art algorithms using both the AR database and FRGC (Face Recognition Grand Challenge) ver2.0 database. Very encouraging experimental results demonstrate, for the first time, the feasibility and effectiveness of a high-level syntactic method in face recognition, showing a new strategy for face representation and recognition.
Chapter PDF
Similar content being viewed by others
References
Bartlett, M., Movellan, J., Sejnowski, T.: Face recognition by independent component analysis. IEEE Transactions on Neural Networks 13(6), 1450–1464 (2002)
Biederman, I., Ju, G.: Surface versus edge-based determinants of visual recognition. Cognitive Psychology 20, 38–64 (1988)
Bruce, V., Hanna, E., Dench, N., Healey, P., Burton, M.: The importance of mass in line drawings of faces. Applied Congnitive Psychology 6(7), 619–628 (1992)
Chen, S.W., Tung, S.T., Fang, C.Y., Cheng, S., Jain, A.K.: Extended attributed string matching for shape recognition. Computer Vision and Image Understanding 70(1), 36–50 (1998)
Gao, Y., Leung, M.K.: Face recognition using line edge map. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(6), 764–779 (2002)
Gao, Y., Leung, M.K.: Human face profile recognition using attributed string. Pattern Recognition 35, 353–360 (2002)
Gao, Y., Qi, Y.: Robust visual similarity retrieval in single model face databases. Pattern Recognition 38(7), 1009–1020 (2005)
Kanan, H.R., Faez, K., Gao, Y.: Face recognition using adaptively weighted patch pzm array from a single exemplar image per person. Pattern Recognition 41(12), 3799–3812 (2008)
Leung, M., Yang, Y.: Dynamic two-strip algorithm in curve fitting. Pattern Recognition 23(1-2), 69–79 (1990)
Li, S., Hou, X.W., Zhang, H.J., Cheng, Q.S.: Learning spatially localized, parts-based representation. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. I-207–I-212 (2001)
Martínez, A.: Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(6), 748–763 (2002)
Martínez, A., Benavente, R.: The AR face database. CVC Technical Report 24 (1998)
Needleman, S.B., Wunsch, C.D.: A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol. 48(3), 443–453 (1970)
Penev, P.S., Atick, J.J.: Local feature analysis: A general statistical theory for object representation. Network: Computation in Neural Systems 7, 477–500 (1996)
Petrakis, E.G., Diplaros, A., Milios, E.: Matching and retrieval of distorted and occluded shapes using dynamic programming. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(11), 1501–1516 (2002)
Phillips, P., Flynn, P., Scruggs, T., Bowyer, K., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the face recognition grand challenge. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 947–954 (2005)
Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)
Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. Pattern Analysis and Machine Intelligence, IEEE Transactions on 31(2), 210–227 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, W., Gao, Y. (2010). Recognizing Partially Occluded Faces from a Single Sample Per Class Using String-Based Matching. In: Daniilidis, K., Maragos, P., Paragios, N. (eds) Computer Vision – ECCV 2010. ECCV 2010. Lecture Notes in Computer Science, vol 6313. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15558-1_36
Download citation
DOI: https://doi.org/10.1007/978-3-642-15558-1_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15557-4
Online ISBN: 978-3-642-15558-1
eBook Packages: Computer ScienceComputer Science (R0)