A comparison of three methods of face recognition for home photos
Abstract
This poster presents experimental results of three face recognition methods -- Support Vector Machine (SVM), Local Binary Pattern (LBP)-based, and Sparse Represented-based Classification (SRC). We will show the experimental results based on AR face database and on home photos. The experiments show that the three algorithms can achieve over 85% recognition rate in AR database. However, the recognition rate is extremely reduced in home photos. SVM and SRC-based method encounter challenges of selecting training model while LBP-based method encounters the challenge of merging over scattered clusters. Our goal is to improve the accuracy and efficiency especially in home photos based on the three methods.
References
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P. J. Phillips. Support vector machines applied to face recognition. In M. S. Kearns, S. A. Solla, and D. A. Cohn, editors, NIPS'98, 1998.
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Zhou, Y., Gu, L. and Zhang, H. Bayesian Tangent Shape Model: Estimating Shape and Pose Parameters via Bayesian Inference. CVPR 2003.
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Ahonen, T., Hadid, A. and Pietikainen, M. Face Description with Local Binary Patterns: Application to Face Recognition. PAMI 2006.
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Wright, J., Yang, A. Y., Ganesh, A., Sastry, S. S. and Yi Ma. Robust Face Recognition via Sparse Representation. PAMI 2008.
Index Terms
- A comparison of three methods of face recognition for home photos
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Published In
August 2009
103 pages
ISBN:9781450379281
DOI:10.1145/1599301
Copyright © 2009 Copyright is held by the owner/author(s).
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 03 August 2009
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- Research-article
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SIGGRAPH09
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SIGGRAPH09: Special Interest Group on Computer Graphics and Interactive Techniques Conference
August 3 - 7, 2009
Louisiana, New Orleans
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Overall Acceptance Rate 1,822 of 8,601 submissions, 21%
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