Face verification, though for humans seems to be an easy task, is a long-standing research area. With challenging covariates such as disguise or face obfuscation, automatically verifying the identity of a person is assumed to be very hard. This paper explores the feasibility of face verification under disguise variations using multi-spectrum (visible and thermal) face images. We propose a framework, termed as Aravrta1, which classifies the local facial regions of both visible and thermal face images into biometric (regions without disguise) and non-biometric (regions with disguise) classes. The biometric patches are then used for facial feature extraction and matching. The performance of the algorithm is evaluated on the IHTD In and Beyond Visible Spectrum Disguise database that is prepared by the authors and contains images pertaining to 75 subjects with different kinds of disguise variations. The experimental results suggest that the proposed framework improves the performance compared to existing algorithms, however there is a need for more research to address this important covariate.
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