Mean Laplacian mappings-based difference LDA for face recognition
- 180 Downloads
This paper proposes a difference LDA based on mean Laplacian mappings. For each pixel, we firstly estimate multiple mean Laplacian mappings which include an odd and even and full mean Laplacian mappings, and generate three different images respectively. Then, we obtain a concatenated image by concatenating the odd, even and full images. The usage of the concatenated mean Laplacian mapping results in a more robust dissimilarity measures between images. In order to derive discriminative representation for the concatenated feature vector, we introduce a difference LDA which applies a difference scatter matrix to find the subspace that best discriminates different face classes. The introduction of the difference scatter matrix avoids the singularity of the within-class scatter matrix. Experiments show that the proposed method for facial expression, illumination change and different occlusion has better robustness, and achieves a higher recognition rate. For a single sample per person, the proposed method can also obtain a higher recognition rate.
KeywordsMean Laplacian mappings Difference scatter matrix Linear discriminant analysis Robust dissimilarity measures Face recognition
The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of this paper. This work has been supported by PhD research startup foundation of Shenyang Aerospace University(Grant No. 15YB05), Foundation of Liaoning Educational Committee (Grant No. L2015403), Technology Innovation Foundation (Basic Research) of Aviation Industry Corporation of China(Grant No. 2013S60109R), and National Natural Science Foundation of China (Grant No. 61170185, 61303016).
- 1.Balasubramanian M, Schwartz EL, Tenenbaum JB, de Silva V, Langford JC (2002) The isomap algorithm and topological stability. Science 295(5552)7Google Scholar
- 6.Cai D, He X, Zhou K, Han J, Bao H (2007) Locality sensitive discriminant analysis. In the 20th International Joint Conference on Artificial Intelligence(IJCAI)Google Scholar
- 7.Cai D, He X, Han J (2007) Semi-supervised discriminant analysis, in: IEEE 11th International Conference on Computer Vision (ICCV)Google Scholar
- 8.Cai D, He X, Han J (2007) Isometric projection. In Proceedings of AAAI Conference on Artificial IntelligenceGoogle Scholar
- 11.Fu Y, Huang T (2005) Locally linear embedded eigenspace analysis, IFP-TR, University of Illinois at Urbana-Champaign, JanuaryGoogle Scholar
- 13.He XF, Cai D, Yan SC and Zhang HJ (2005) Neighborhood preserving embedding. In IEEE Int’l Conf. on Computer Vision (ICCV)Google Scholar
- 16.Li ZK, Ding LX, He JR, Hu QH (2014) Face feature representation based on image decomposition. J Softw 25(9):2102–2118 (in Chinese) Google Scholar
- 17.Li Z, Ding L, Wang Y, et al (2014) Face representation with gradient orientations and euler mapping: application to face recognition. Int J Pattern Recognit Artif Intell 28(08)Google Scholar