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Virtual Journal for Biomedical Optics

Virtual Journal for Biomedical Optics


  • Editors: Andrew Dunn and Anthony Durkin
  • Vol. 9, Iss. 3 — Mar. 6, 2014

Self-training-based face recognition using semi-supervised linear discriminant analysis and affinity propagation

Haitao Gan, Nong Sang, and Rui Huang  »View Author Affiliations

JOSA A, Vol. 31, Issue 1, pp. 1-6 (2014)

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Face recognition is one of the most important applications of machine learning and computer vision. The traditional supervised learning methods require a large amount of labeled face images to achieve good performance. In practice, however, labeled images are usually scarce while unlabeled ones may be abundant. In this paper, we introduce a semi-supervised face recognition method, in which semi-supervised linear discriminant analysis (SDA) and affinity propagation (AP) are integrated into a self-training framework. In particular, SDA is employed to compute the face subspace using both labeled and unlabeled images, and AP is used to identify the exemplars of different face classes in the subspace. The unlabeled data can then be classified according to the exemplars and the newly labeled data with the highest confidence are added to the labeled data, and the whole procedure iterates until convergence. A series of experiments on four face datasets are carried out to evaluate the performance of our algorithm. Experimental results illustrate that our algorithm outperforms the other unsupervised, semi-supervised, and supervised methods.

© 2013 Optical Society of America

OCIS Codes
(100.5010) Image processing : Pattern recognition
(150.2950) Machine vision : Illumination
(100.3008) Image processing : Image recognition, algorithms and filters

ToC Category:
Image Processing

Original Manuscript: June 4, 2013
Revised Manuscript: October 9, 2013
Manuscript Accepted: November 6, 2013
Published: December 2, 2013

Virtual Issues
Vol. 9, Iss. 3 Virtual Journal for Biomedical Optics

Haitao Gan, Nong Sang, and Rui Huang, "Self-training-based face recognition using semi-supervised linear discriminant analysis and affinity propagation," J. Opt. Soc. Am. A 31, 1-6 (2014)

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  1. A. F. Abate, M. Nappi, D. Riccio, and G. Sabatino, “2D and 3D face recognition: a survey,” Pattern Recogn. Lett. 28, 1885–1906 (2007). [CrossRef]
  2. R. Jafri and H. R. Arabnia, “A survey of face recognition techniques,” J. Inf. Process. Syst. 5, 41–68 (2009). [CrossRef]
  3. D. Cai, X. He, Y. Hu, J. Han, and T. Huang, “Learning a spatially smooth subspace for face recognition,” in Proceedings of IEEE Conference Computer Vision and Pattern Recognition (IEEE, 2007), pp. 1–7.
  4. X. He, S. Yan, Y. Hu, P. Niyogi, and H.-J. Zhang, “Face recognition using laplacianfaces,” IEEE Trans. Patt. Analysis Mach. Intell. 27, 328–340 (2005). [CrossRef]
  5. J. Lu, Y.-P. Tan, and G. Wang, “Discriminative multimanifold analysis for face recognition from a single training sample per person,” IEEE Trans. Patt. Analysis Mach. Intell. 35, 39–51 (2013). [CrossRef]
  6. R. Gross, I. Matthews, and S. Baker, “Appearance-based face recognition and light-fields,” IEEE Trans. Patt. Analysis Mach. Intell. 26, 449–465 (2004). [CrossRef]
  7. H. K. Ekenel and R. Stiefelhagen, “Local appearance-based face recognition using discrete cosine transform,” in 13th European Signal Processing Conference (EUSIPCO, 2005).
  8. H. Murase and S. K. Nayar, “Visual learning and recognition of 3D objects from appearance,” Int. J. Comput. Vis. 14, 5–24 (1995). [CrossRef]
  9. Z. Lei, S. Liao, M. Pietikainen, and S. Z. Li, “Face recognition by exploring information jointly in space, scale and orientation,” IEEE Trans. Image Process. 20, 247–256 (2011). [CrossRef]
  10. W. Yu, X. Teng, and C. Liu, “Face recognition using discriminant locality preserving projections,” Image Vis. Comput. 24, 239–248 (2006). [CrossRef]
  11. C. Rosenberg, M. Hebert, and H. Schneiderman, “Semi-supervised self-training of object detection models,” in Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (IEEE, 2005), pp. 29–36.
  12. A. Blum and T. Mitchell, “Combining labeled and unlabeled data with co-training,” in Proceedings of the Eleventh Annual Conference on Computational Learning Theory (ACM, 1998), pp. 92–100.
  13. T. Joachims, “Transductive inference for text classification using support vector machines,” in Proceedings of the Sixteenth International Conference on Machine Learning (Morgan Kaufmann Publishers, 1999), pp. 200–209.
  14. K. P. Nigam, “Using unlabeled data to improve text classification,” Ph.D. thesis (Carnegie Mellon University, 2001). AAI3040487.
  15. X. Zhu, Z. Ghahramani, and J. Lafferty, “Semi-supervised learning using Gaussian fields and harmonic functions,” in Proceedings of the 20th International Conference on Machine Learning (Morgan Kaufmann Publishers, 2003), pp. 912–919.
  16. X. Zhu, “Semi-supervised learning literature survey,” (Computer Sciences, University of Wisconsin-Madison, 2005).
  17. F. Roli and G. Marcialis, “Semi-supervised PCA-based face recognition using self-training,” in Structural, Syntactic, and Statistical Pattern Recognition, Vol. 4109 of Lecture Notes in Computer Science (Springer, 2006), pp. 560–568.
  18. A. M. Martinez and A. Kak, “PCA versus LDA,” IEEE Trans. Patt. Analysis Mach. Intell. 23, 228–233 (2001). [CrossRef]
  19. X. Zhao, N. W. D. Evans, and J.-L. Dugelay, “Semi-supervised face recognition with LDA self-training,” in IEEE International Conference on Image Processing (IEEE, 2011), pp. 3102–3105.
  20. D. Cai, X. He, and J. Han, “Semi-supervised discriminant analysis,” in Proceedings of International Conference Computer Vision (IEEE, 2007).
  21. B. J. Frey and D. Dueck, “Clustering by passing messages between data points,” Science 315, 972–976 (2007). [CrossRef]
  22. Y. Fujiwara, G. Irie, and T. Kitahara, “Fast algorithm for affinity propagation,” in Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence (AAAI, 2011), pp. 2238–2243.
  23. F. Kschischang, B. Frey, and H.-A. Loeliger, “Factor graphs and the sum-product algorithm,” IEEE Trans. Inf. Theory 47, 498–519 (2001). [CrossRef]
  24. P. Belhumeur, J. Hespanha, and D. Kriegman, “Eigenfaces versus fisherfaces: recognition using class specific linear projection,” IEEE Trans. Patt. Analysis Mach. Intell. 19, 711–720 (1997). [CrossRef]
  25. M. Turk and A. Pentland, “Eigenfaces for recognition,” J. Cogn. Neurosci. 3, 71–86 (1991).
  26. ORL face dataset, http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html .
  27. Yale face dataset, http://cvc.yale.edu/projects/yalefaces/yalefaces.html .
  28. Extended Yale face dataset B, http://www.cad.zju.edu.cn/home/dengcai/Data/FaceData.html .
  29. D. Cai, X. He, J. Han, and H.-J. Zhang, “Orthogonal laplacianfaces for face recognition,” IEEE Trans. Image Process. 15, 3608–3614 (2006). [CrossRef]
  30. D. Cai, X. He, and J. Han, “Spectral regression for efficient regularized subspace learning,” in IEEE 11th International Conference on Computer Vision (IEEE, 2007), pp. 1–8.
  31. CMU PIE face dataset, http://vasc.ri.cmu.edu/idb/html/face/ .

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