Collaborative representation of Grassmann manifold projection metric for robust multi-spectral face recognition

Abstract

Spectral face recognition is gaining importance as the information from different bands can lead to robust face representation that are presentation (a.k.a, spoofing) attack resistant. However, the key challenge here is to process the high dimensional spatio-spectral data to extract and represent the reliable data while discarding redundant data information for processing. In this work, we present a new approach to represent the spectral images in a holistic manner by employing the well-known Projection Metric Learning (PML) in Grassmann manifold such that the redundant information from the spectral data is discarded while retaining significant information. The approach is adopted to learn discriminative information from an high dimensional spectral dataset. Further, we propose an extension using collaborative representation of learnt projection metrics for improving the classification accuracy of spectral data. With the extensive set of experiments conducted on a relatively large scale extended-spectral face image database (6048 images) of 168 subjects, we demonstrate the applicability of the proposed framework. The obtained results indicates highest accuracy by achieving a Rank-1 recognition rate of 98.21 percent in classifying the spectral face images.