“A comparison of three methods of face recognition for home photos” by Yeh, Shih, Lin, Lu, Chang, et al. …
Conference:
Type(s):
Title:
- A comparison of three methods of face recognition for home photos
Presenter(s)/Author(s):
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:
1. 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.
2. Zhou, Y., Gu, L. and Zhang, H. Bayesian Tangent Shape Model: Estimating Shape and Pose Parameters via Bayesian Inference. CVPR 2003.
3. Ahonen, T., Hadid, A. and Pietikainen, M. Face Description with Local Binary Patterns: Application to Face Recognition. PAMI 2006.
4. Wright, J., Yang, A. Y., Ganesh, A., Sastry, S. S. and Yi Ma. Robust Face Recognition via Sparse Representation. PAMI 2008.
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