In recent years, face recognition in the unconstrained environment has attracted increasing
attentions, and a few methods have been evaluated on the Labeled Faces in the Wild (LFW) …

There are two fundamental problems with the linear discriminant analysis (LDA) for face
recognition. First one is LDA is not stable because of the small training sample size problem …

Discriminant analysis is an important technique for face recognition because it can extract
discriminative features to classify different persons. However, most existing discriminant …

In face recognition, the Fisherface approach based on Fisher linear discriminant analysis
(FLDA) has obtained some success. However, FLDA fails when each person just has one …

The complete linear discriminant analysis (CLDA) algorithm has been successfully
employed for face recognition. The CLDA method can make full use of the discriminant …

This paper proposes a novel face recognition method that improves Huang's linear
discriminant regression classification (LDRC) algorithm. The original work finds a …

The Fisherface is one of the most successful face recognition methods, which however,
cannot be directly applied to face recognition where only one sample image per person is …

Face recognition has witnessed significant progress due to the advances of deep
convolutional neural networks (CNNs), the central task of which is how to improve the …

Face recognition (FR) with a single sample per person (SSPP) is one of the most
challenging problems in computer vision. In this scenario, it is difficult to predict facial …

Linear discriminant analysis (LDA) often suffers from the small sample size problem when
dealing with high-dimensional face data. Random subspace can effectively solve this …