Abstract
To solve the problem of the singularity of the within-class scatter matrix in discriminant local median preserving projections (DLMPP) in the case of small sample size problem, an algorithm named generalized local median preserving projection (GDLMPP) is proposed. To solve the small size problem, GDLMPP firstly transforms the samples into a lower dimensional space equivalently, and then the optimal projection matrix can be solved. The theoretical analysis shows that GDLMPP is equivalent to DLMPP when the within-class scatter matrix is non-singular. Finally, we conduct extensive experiments to prove that the proposed algorithm can provide a better representation and achieve higher face recognition rates than previous approaches such as LPP, LDA and DLMPP on the ORL, Yale and AR face databases.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Yang W, Sun C, Zhang L (2011) A multi-manifold discriminant analysis method for image feature extraction. Pattern Recogn 44(8):1649–1657
Yang W, Wang J, Ren M et al (2009) Feature extraction based on Laplacian bidirectional maximum margin criterion. Pattern Recogn 42(11):2327–2334
Yang W, Wang Z, Sun C (2015) A collaborative representation based projections method for feature extraction. Pattern Recogn 48(1):20–27
Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86
Vidal R, Ma Y, Sastry SS (2016) Robust principal component analysis. Generalized principal component analysis. Springer, New York, pp 63–122
Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720
Wang S, Lu J, Gu X et al (2016) Semi-supervised linear discriminant analysis for dimension reduction and classification. Pattern Recogn 57:179–189
He XF, Yan SC, Hu YX et al (2005) Face recognition using Laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340
Wen Y, Yang S, Hou L, et al (2016) Face recognition using locality sparsity preserving projections. In: 2016 international joint conference on neural networks (IJCNN), IEEE, pp 3600–3607
Yu WW, Teng XL, Liu CQ (2006) Face recognition using discriminant locality preserving projections. Image Vis Comput 24(3):239–248
Huang P, Tang Z (2012) Discriminant of local median preserving projection with its application to face recognition. J Comput Aided Des Comput Graph 24(11):1420–1425
Wan M, Li M, Yang G, Gai S, Jin Z (2014) Feature extraction using two-dimensional maximum embedding difference. Inf Sci 274:55–69
Lai Z, Wong W, Xu Y, Yang J, Tang J, Zhang D (2016) Approximate orthogonal sparse embedding for dimensionality reduction. IEEE Trans Neural Netw Learn Syst 27(4):723–735
Ning X, Li W, LI H et al (2016) Uncorrelated local preserving discriminant analysis based on bionics. J Comput Res Dev 53(11):2623–2629
Ma X, Tan Y (2014) Face recognition based on discriminant sparse preserving embedding. Acta Automatica Sinica 40(1):73–82
Zhao Z, Hao X (2013) Linear locality preserving and discriminating projection for face recognition. J Electron Inf Technol 35(2):463–467
Yin J, Zeng W, Wei L (2016) Optimal feature extraction methods for classification methods and their applications to biometric recognition. Knowl Based Syst 99:112–122
Yin J, Wei L, Song M, Zeng W (2016) Optimized projection for collaborative representation based classification and its applications to face recognition. Pattern Recogn Lett 73:83–90
Wan M, Lai Z, Yang G et al (2017) Local graph embedding based on maximum margin criterion via fuzzy set. Fuzzy Sets Syst 318:120–131
Yang J, Zhang D, Yang J et al (2007) Globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics. IEEE Trans Pattern Anal Mach Intell 29(4):650–664
Acknowledgements
This work is partially supported by National Key R&D Program Grant No. 2017YFC0804002, the National Science Foundation of China under Grant Nos. 61462064, 6177227, 61362031, 61463008, 61403188, 61503195, 61603192, and the China Postdoctoral Science Foundation under Grant No. 2016M600674, the Natural Science Fund of Jiangsu Province under Grant BK20161580, BK20171494 and China’s Aviation Science (No. 20145556011).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wan, MH., Lai, ZH. Generalized Discriminant Local Median Preserving Projections (GDLMPP) for Face Recognition. Neural Process Lett 49, 951–963 (2019). https://doi.org/10.1007/s11063-018-9840-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11063-018-9840-6