Two phase representation discriminant projection for face recognition

Abstract

This paper proposes a novel approach called two-phase representation discriminant projection (TPRDP) for facial recognition. It uses two-phase representation learning to obtain a robust representation for each training sample. In the first phase, the method identifies a set of closely related samples to the training sample based on representation-based distances. In the second phase, TPRDP uses these samples to linearly encode the training sample, effectively enhancing its representation capability. After that, TPRDP constructs two graphs to capture relationships between samples. By leveraging these graphs, TPRDP seeks an optimized transformation matrix that enhances both within-class compactness and between-class separability. Through the projection of samples into a subspace using this matrix, TPRDP effectively improves the discrimination between different classes while preserving compactness within each class. Comprehensive experiments on various face datasets, including AR, CMU PIE, and ExtYaleB, confirm the effectiveness of our method.