Sparsity preserving discriminant analysis for single training image face recognition

: Single training image face recognition is one of main challenges to appearance-based pattern recognition techniques. Many classical dimensionality reduction methods such as LDA have achieved success in face recognition field, but can not be directly used to the single training image scenario. Recent graph-based semi-supervised dimensionality reduction (SSDR) provides a feasible strategy to deal with such problem. However, most of the existing SSDR algorithms such as Semi-supervised Discriminant Analysis (SDA) are locality-oriented and generally suffer from the following issues: 1) they need a large number of unlabeled training samples to estimate the manifold structure in data, but such extra samples may not be easily obtained in a given face recognition task; 2) they model the local geometry of data by the nearest neighbor criterion which generally fails to obtain sufficient discriminative information due to the high-dimensionality of face image space; 3) they construct the underlyi...

• Type: Journal
• Year: 2010
• Where: PRL
• Authors: Lishan Qiao, Songcan Chen, Xiaoyang Tan
• Keywords: Dimensionality Reduction, Face Recognition, PRL 2010, Single Training Image