Collaborative Discriminant Locality Preserving Projections With its Application to Face Recognition

TL;DR

This paper introduces CDLPP, a novel face recognition algorithm that enhances discriminative power by optimizing class separation and leveraging collaborative representation, showing superior results on multiple face datasets.

Contribution

The paper proposes CDLPP, which improves DLPP by ensuring global class separation and incorporating an $L_2$-norm constraint for better feature collaboration.

Findings

CDLPP outperforms existing methods on AR, ORL, and LFW-A face datasets.

The algorithm significantly improves discriminative capability over traditional DLPP.

Experimental results demonstrate state-of-the-art performance in face recognition.

Abstract

We present a novel Discriminant Locality Preserving Projections (DLPP) algorithm named Collaborative Discriminant Locality Preserving Projection (CDLPP). In our algorithm, the discriminating power of DLPP are further exploited from two aspects. On the one hand, the global optimum of class scattering is guaranteed via using the between-class scatter matrix to replace the original denominator of DLPP. On the other hand, motivated by collaborative representation, an $L_2$-norm constraint is imposed to the projections to discover the collaborations of dimensions in the sample space. We apply our algorithm to face recognition. Three popular face databases, namely AR, ORL and LFW-A, are employed for evaluating the performance of CDLPP. Extensive experimental results demonstrate that CDLPP significantly improves the discriminating power of DLPP and outperforms the state-of-the-arts.