In the past decade or so, subspace methods have been largely used in face recognition ? generally with quite success. Subspace approaches, however, generally assume the training d...
Previous works have demonstrated that the face recognition performance can be improved significantly in low dimensional linear subspaces. Conventionally, principal component analy...
Topic models have recently emerged as powerful tools for modeling topical trends in documents. Often the resulting topics are broad and generic, associating large groups of people...
Vidit Jain, Erik G. Learned-Miller, Andrew McCallu...
When applied to high-dimensional classification task such as face recognition, linear discriminant analysis (LDA) can extract two kinds of discriminant vectors, those in the null s...
Wangmeng Zuo, Kuanquan Wang, David Zhang, Jian Yan...
This paper presents a 3D approach for recognizing faces based on Principal Component Analysis (PCA). The approach addresses the issue of proper 3D face alignment required by PCA f...