Hierarchical extended collaborative representation based classification for single‐sample face recognition

Abstract

Collaborative representation based classification (CRC) has been widely used and shown good performance in face recognition (FR). Afterwards, hierarchical representation based classification has recently been proposed and aims to enhance the classification performance of the CRC method. However, these methods highly depend on the over‐complete dictionary comprised of sufficient training samples, and cannot be directly applied for single‐sample FR. In this study, the authors propose a novel CRC‐based FR framework to address this issue, which is named hierarchical extended collaborative representation based classification (HECRC). Firstly, they integrate hierarchical representation based model with low‐rank constrained variation dictionaries. Secondly, they select training samples that are the nearest neighbours of test images to obtain a more discriminative training dictionary, where an adaptive scheme is introduced to select proper samples automatically instead of setting a predefined number in traditional methods. Finally, the refined training dictionary and the learned variation dictionaries are jointly utilised to represent the test sample. Moreover, they combined the proposed HECRC with deep features to further improve the recognition rate. Experiments have been conducted on the AR and FERET datasets, and the results show that the proposed method has a substantial improvement over existing algorithms for single‐sample FR.

Keywords

• sufficient training samples
• single-sample FR
• novel CRC-based FR framework
• hierarchical extended collaborative representation
• low-rank constrained variation dictionaries
• discriminative training dictionary