A Doubly Weighted Approach for Appearance-Based Subspace Learning Methods

We propose in this paper a doubly weighted subspace learning approach for face representation and recognition. Motivated by the fact that some face samples and parts are more effectual in characterizing and recognizing faces, we construct two weighting matrices based on pairwise similarity of face samples within a same class and discriminant score of each pixel within a face sample to duly emphasize both the between-sample and within-sample features. We then incorporate these two weighting matrices into three popular subspace learning methods, namely principal component analysis, linear discriminant analysis, and nonnegative matrix factorization, to obtain the discriminative features of faces for recognition. Moreover, the proposed doubly weighted technique can be readily extended to other newly proposed subspace learning algorithms to improve their performance. Experimental results show that the proposed approach can effectively enhance the discriminant power of the extracted face features and outperform existing, nonweighted subspace learning algorithms. The performance gain is even more apparent for cases with imbalanced training samples.