Multi-view face recognition based on manifold learning and multilinear representation

摘要：

We propose an improved Tensorfaces algorithm for multi-view face recognition which integrates multi-linear analysis,manifold learning and statistical clustering in one framework.The training face images from different views are first mapped into a 2-D space by the Locality Preserving Projections (LPP) method where statistical clustering is used to capture the view variability.Then a test image of an unknown view can be projected into this 2-D space,and the two closet views can be identified.We develop a modified tensor decomposition method by incorporating two closest views in the calculation of the identity coefficients.The proposed method is evaluated on a large database of multi-view face images that include the CMU PIE and Weizmann databases.Experimental results show that this method outperforms the original TensorFaces method.

作者: JIANG Shan SHUANG Kai FAN Guoliang TIAN Chunna WANG Yu

作者单位: China University ofPetroleum Beijing 102249 P.R.China Oklahoma State UniversityStillwater,OK 74078 Xidian University 710071,China China University of Petroleum Beijing 102249 P.R.China

会议类型: 国际会议

会议名称: 9th International Conference on Signal Processing(第九届国际信号处理学术会议)(ICSP08)

会议地点: 北京

会议语种:英文

在线出版日期: 2008-10-26（万方平台首次上网日期，不代表论文的发表时间）