Multilinear Supervised Neighborhood Embedding with Local Descriptor Tensor for Face Recognition

Xian-Hua HAN  Xu QIAO  Yen-Wei CHEN  

IEICE TRANSACTIONS on Information and Systems   Vol.E94-D   No.1   pp.158-161
Publication Date: 2011/01/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E94.D.158
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Pattern Recognition
tensor analysis,  supervised neighborhood embedding,  subspace learning,  local SIFT feature,  view-based object recognition,  

Full Text: PDF>>
Buy this Article

Subspace learning based face recognition methods have attracted considerable interest in recent years, including Principal Component Analysis (PCA), Independent Component Analysis (ICA), Linear Discriminant Analysis (LDA), and some extensions for 2D analysis. However, a disadvantage of all these approaches is that they perform subspace analysis directly on the reshaped vector or matrix of pixel-level intensity, which is usually unstable under illumination or pose variance. In this paper, we propose to represent a face image as a local descriptor tensor, which is a combination of the descriptor of local regions (K*K-pixel patch) in the image, and is more efficient than the popular Bag-Of-Feature (BOF) model for local descriptor combination. Furthermore, we propose to use a multilinear subspace learning algorithm (Supervised Neighborhood Embedding-SNE) for discriminant feature extraction from the local descriptor tensor of face images, which can preserve local sample structure in feature space. We validate our proposed algorithm on Benchmark database Yale and PIE, and experimental results show recognition rate with our method can be greatly improved compared conventional subspace analysis methods especially for small training sample number.