Abstract
A novel efficient algorithm called unsupervised multi-manifold linear differential projection(UMLDP) is proposed to overcome the drawbacks of existing unsupervised linear differential projection(ULDP) for face recognition. Firstly, the multi-manifold local neighborhood graph and the largest global variance is constructed respectively. Next, we calculate a low dimensional manifold embedded in high-dimensional space through the multi-objective optimization. This mapping can not only get the low-dimensional manifolds embedded in a high-dimensional space but also maintain the local and the global structural information effectively. Finally, experimental results validate the effectiveness of the proposed algorithm on the ORL, Yale and AR face databases.






Similar content being viewed by others
References
Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12):2037–2041
Bartlett MS, Lades HM, Sejnowski TJ (2002) Face recognition by independent component analysis. IEEE Trans Neural Netw 13(6):1450–1464
Gu B, Sheng VS, Tay KY, Romano W, Li S (2014) Incremental support vector learning for ordinal regression. IEEE Trans Neural Netw Learn Syst 26(7):1403–1416
He XF, Yan SC, Hu Y et al (2005) Face Recognition using Laplacian faces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340
Lai Z, Wong WK, Yang J et al (2016) Approximate orthogonal sparse embedding for dimensionality reduction. IEEE Trans Neural Netw Learn Syst 27(4):723–735
Lai ZH, Xu Y, Chen QC et al (2014) Multi-linear sparse principal component analysis. IEEE Trans Neural Netw Learn Syst 25(10):1942–1950
Lai ZH, Xu Y, Jin Z et al (2014) Human gait recognition via sparse discriminant projection learning. IEEE Trans Circuits Syst Video Technol 24(10):1651–1662
Li H, Jiang T, Zhang K (2006) Efficient and robust feature extraction by maximum margin criterion. IEEE Trans Neural Netw 17(1):157–165
Lu ZH, Lu SY, Liu G, Zhang YD et al (2016) A pathological brain detection system based on radial basis function neural network. J Med Imaging Health Inform 6:1218–1222
Lu J, Tan YP, Wang G (2013) Discriminative multi-manifold analysis for face recognition from a single training sample per person. IEEE Trans Pattern Anal Mach Intell 35(1):39–51
Ma XH, Tan YQ (2014) Face recognition based on discriminant sparsity preserving embedding. Acta Automat Sin 40(1):73–82
Nixon M, Aguado A (2008) Feature extraction and image processing, 2nd edn. Academic Press, Cambridge, pp 385–398
Roweis ST, Saul LK (2000) Nonlinear dimensional reduction by locally linear embedding. Science 290(550):2323–2326
Turk M, Pentland A (1991) Eigen faces for recognition. J Cogn Neurosci 3(1):71–86
Wan MH, Lai ZH, Jin Z (2011) Locally minimizing embedding and globally maximizing variance: unsupervised linear difference projection for dimensionality reduction. Neural Process Lett 33(3):267–282
Wan MH, Li M, Yang GW et al (2014) Feature extraction using two-dimensional maximum embedding difference. Inf Sci 274:55–69
Wang SH, Yang M, Du SD et al (2016) Wavelet entropy and directed acyclic graph support vector machine for detection of patients with unilateral hearing loss in MRI scanning. Front Comput Neurosci. doi:10.3389/fncom.2016.00106
Wen X, Shao L, Xue Y, Fang W (2015) A rapid learning algorithm for vehicle classification. Inf Sci 295(1):395–406
Wolf L (2011) HassnerT, Taigman Y. Effective unconstrained face recognition by combining multiple descriptors and learned background statistics. IEEE Trans Pattern Anal Mach Intell 33(10):1978–1990
Xia J, Chanussot J, Du P et al (2015) Spectral–spatial classification for hyperspectral data using rotation forests with local feature extraction and Markov random fields. IEEE Trans Geosci Remote Sens 53(5):2532–2546
Yang W, Sun C, Zhang L (2010) Face recognition using a multi-manifold discriminant analysis method. In: Proceedings of IEEE International Conference on Pattern Recognition (ICPR), 527–530
Yang W, Sun C, Zhang L (2011) A multi-manifold discriminant analysis method for image feature extraction. Pattern Recogn 44(8):1649–1657
Yang X, Wu W, Qing L et al (2009) Image feature extraction and matching technology. Opt Precis Eng 9:33–33
Yang J, Zhang D, Yang JY 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
Yang J, Zhang D, Yang JY (2004) Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans Pattern Anal Mach Intell 26(1):131–137
Yu H, Yang J (2001) A direct LDA algorithm for high-dimensional data-with application to face recognition. Pattern Recogn 34(10):2067–2070
Yuan C, Sun X, Rui L (2016) Fingerprint liveness detection based on multi-scale LPQ and PCA. China Commun 13(7):60–65
Zhang YD, Chen XQ, Zhan TM et al (2016) Fractal dimension estimation for developing pathological brain detection system based on minkowski-bouligand method. IEEE Access 4:5937–5947
Zhang BC, Gao YS, Zhao SQ et al (2010) Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor. IEEE Trans Image Process 19(2):533–544
Zhang YD, Lu SY, Zhou XX et al (2016) Comparison of machine learning methods for stationary wavelet entropy-based multiple sclerosis detection: decision tree, k-nearest neighbors, and support vector machine. Simulation 92(9):861–871
Zhang YD, Wu XY, Lu SY et al (2016) Smart detection on abnormal breasts in digital mammography based on contrast-limited adaptive histogram equalization and chaotic adaptive real-coded biogeography-based optimization. Simulation 92(9):873–885
Zhang P, You X, Ou W et al (2016) Sparse discriminative multi-manifold embedding for one-sample face identification. Pattern Recogn 52:249–259
Zheng Y, Byeungwoo J, Xu D et al (2015) Image segmentation by generalized hierarchical fuzzy C-means algorithm. J Intell Fuzzy Syst 28(2):4024–4028
Acknowledgments
This work is supported by the National Natural Science Fund of China (Grant Nos. 61503195, 61462064, 61203243,61402231, 61603192 and 61272077), the Natural Science Fund of Jiangsu Province(Grant No. BK20161580), the University Natural Science Fund of JiangSu Province, China (Grant No.15KJB520018, 16KJB520020 and 12KJA63001).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yang, Z., Wan, M., Zhan, T. et al. Unsupervised multi-manifold linear differential projection(UMLDP) for face recognition. Multimed Tools Appl 77, 3795–3811 (2018). https://doi.org/10.1007/s11042-016-4105-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-016-4105-8