Collaborative Representation of Statistically Independent Filters' Response: An Application to Face Recognition Under Illicit Drug Abuse Alterations

Face biometrics is widely deployed in many security and surveillance applications that demand a secure and reliable authentication service. The performance of face recognition systems is primarily based on the analysis of texture and geometric variation of the face. Continuous and extensive consumption of illicit drugs will significantly result in deformation of both texture and geometric characteristics of a face and thus, impose additional challenges on accurately identifying the subjects who abuse drugs. This work proposes a novel scheme to improve robustness of face recognition system to address the variations caused by the prolonged use of illicit drugs. The proposed scheme is based on the collaborative representation of statistically independent filters whose responses are computed on the face images captured before and after substance (or drug) abuse. Extensive experiments are carried out on the publicly available Illicit Drug Abuse Database (DAD) comprised of face images from 100 subjects. The obtained results indicate better performance of the proposed scheme when compared with six different state-of-the-art approaches including a commercial face recognition system.