Biometric Uncorrelated Face Recognition Using Unsupervised Learning
Main Article Content
Abstract
In this paper we developed a new dimensionality reduction method, named Biomimetic Uncorrelated Locality Discriminate Projection (BULDP), for face recognition. It is based on unsupervised discriminate projection and two human bionic characteristics: principle of homology continuity and principle of heterogeneous similarity. With these two human bionic characteristics, we propose a novel adjacency coefficient representation, which does not only capture the category information between different samples, but also reflects the continuity between similar samples and the similarity between different samples. By applying this new adjacency coefficient into the unsupervised discriminant projection, it can be shown that we can transform the original data space into an uncorrelated discriminant subspace. A detailed solution of the proposed BULDP is given based on singular value decomposition. Moreover, we also develop a nonlinear version of our BULDP using kernel functions for nonlinear dimensionality reduction. The performance of the proposed algorithms is evaluated and compared with the state-of-the-art methods on four public benchmarks for face recognition. Experimental results show that the proposed BULDP method and its nonlinear version achieve much competitive recognition performance..
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
IJCERT Policy:
The published work presented in this paper is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. This means that the content of this paper can be shared, copied, and redistributed in any medium or format, as long as the original author is properly attributed. Additionally, any derivative works based on this paper must also be licensed under the same terms. This licensing agreement allows for broad dissemination and use of the work while maintaining the author's rights and recognition.
By submitting this paper to IJCERT, the author(s) agree to these licensing terms and confirm that the work is original and does not infringe on any third-party copyright or intellectual property rights.
References
Z. R. Lai, D. Q. Dai, C. X. Ren, and K. K. Huang, “Discriminative and compact coding for robust face recognition,” IEEE Transactions on Cybernetics, vol. 45, pp. 1900–1912, 2015.
J. Lu, V. E. Liong, G. Wang, and P. Moulin, “Joint feature learning for face recognition,” IEEE Transactions on Information Forensics and Security, vol. 10, pp. 1371–1383, 2015.
W. Hwang and J. Kim, “Markov network-based uni?ed classi?er for face recognition,” IEEE Transactions on Image Processing, vol. 24, pp. 4263–4275, 2015.
M. Kafai, L. An, and B. Bhanu, “Reference face graph for face recognition,” IEEE Transactions on Information Forensics and Security, vol. 9, no. 12, pp. 2132–2143, 2014.
M. De Marsico, M. Nappi, D. Riccio, and H. Wechsler, “Robust face recognition for uncontrolled pose and illumination changes,” IEEE Transactions on Systems Man and Cybernetics Systems, vol. 43, no. 1, pp. 149–163, 2013.
T. Zhou and D. Tao, “Double shrinking sparse dimension reduction,” IEEE Transactions on Image Processing, vol. 22, no. 1, pp. 244–257, 2013.
M. Turk and A. Pentland, “Eigenfaces for recognition,” Journal of cognitive neuroscience, vol. 3, no. 1, pp. 71–86, 1991.
P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, “Eigenfaces vs. ?sherfaces: Recognition using class speci?c linear projection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711–720, 1997.
P. Belhumeur, J. Hespanha, and D. Kriegman, “Eigenfaces vs. ?sherfaces: recognition using class speci?c linear projection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711–720, 1997.
L. F. Chen, H. Y. M. Liao, M. T. Ko, J. C. Lin, and G. J. Yu, “A new lda-based face recognition system which can solve the small sample size problem,” Pattern Recognition, vol. 33, no. 99, p. 1713C1726, 2000.
H. Yu and J. Yang, “A direct lda algorithm for high-dimensional data ? with application to face recognition,” Pattern Recognition, vol. 34, no. 00, p. 2067C2070, 2001.
C. Liu and H. Wechsler, “A shape- and texture-based enhanced ?sher classi?er for face recognition,” IEEE Transactions on Image Processing, vol. 10, no. 4, pp. 598–608, 2001.
W. Zhao, A. Krishnaswamy, R. Chellappa, D. L. Swets, and J. Weng, Discriminant analysis of principal components for face recognition. IEEE, 1998.
J. Yang, D. Zhang, A. F. Frangi, and J. Y. Yang, “Two-dimensional pca: a new approach to appearance-based face representation and recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 1, pp. 131–137, 2004.
H. Wang, J. Hu, and W. Deng, “Compressing ?sher vector for robust face recognition,” IEEE Access, vol. PP, no. 99, pp. 1–1, 2017.
E. Begelfor and M. Werman, “The world is not always ?ot or learning curved manifolds,” School of Engineering and Computer Science, Hebrew University of Jerusalem., Tech. Rep, vol. 3, no. 7, p. 8, 2005.
M. Balasubramanian and E. L. Schwartz, “The isomap algorithm and topological stability,” Science, vol. 295, no. 5552, pp. 7–7, 2002.
M. Belkin and P. Niyogi, “Laplacian eigenmaps and spectral techniques for embedding and clustering.” in NIPS, vol. 14, 2001, pp. 585–591.
L. K. Saul and S. T. Roweis, “Think globally, ?t locally: unsupervised learning of low dimensional manifolds,” Journal of Machine Learning Research, vol. 4, pp. 119–155, 2003.
Z.-y. Zhang and H.-y. Zha, “Principal manifolds and nonlinear dimensionality reduction via tangent space alignment,” Journal of Shanghai University (English Edition), vol. 8, no. 4, pp. 406–424, 2004.