A noise-robust semi-supervised dimensionality reduction method for face recognition

Abstract

Face recognition (FR) is a fundamental problem in a biometric identification system and has attracted much attention in pattern recognition and computer vision fields. Since human face images have high dimensionality, dimensionality reduction (DR) is often adapted for FR and a number of relevant methods are proposed, such as principal component analysis (PCA), linear discriminant analysis (LDA). However, face images are generally hard to be labeled and corrupted by noise in the collection phase. To address the problem, we present a novel semi-supervised DR method which is more robust to noise than the traditional DR methods. Our basic idea is that collaborative representation-based classification (CRC) can achieve better performance than the classifier trained by Euclidean distance when the face images are corrupted by noise. In our algorithm, we firstly employ CRC to compute a representation coefficient for each face image. We then construct a reconstruction error-based regularization term through the obtained coefficient vector. Finally, we extend LDA to the semi-supervised framework by embedding the regularization term into LDA. To evaluate the effectiveness of our algorithm, we use four well-known face databases to conduct several experiments by comparing to unsupervised, supervised and semi-supervised DR methods. The results illustrate that our algorithm can always achieve the best performance as the noise corruption percent of noise images increases.