Enhancing Drug Abuse Face Recognition: A Study on Image Corruption and Restoration

Drug abuse poses a pressing societal issue and limits the success of face recognition algorithms. Further, when the faces are acquired in an unconstrained environment, they get affected by several environmental factors including snow, noise, and blur. One prominent limitation of the existing research is that no work has been done to mitigate the impact of drug abuse on deep face recognition. Further, the problem became complicated in the presence of input corruption. Through this study, we aim to fill the gap by investigating the effects of drug-induced facial alterations coupled with input corruptions on deep face recognition. Our study indicates that drug alteration drastically alters facial features and on top of that the corruption further degrades the image quality and hence leads to poor performance by deep face recognition networks. Therefore, the one question we asked inspired by the success of generative networks is whether these models can help mitigate the impact of drug abuse and image corruption to boost performance. Through experiments simulating real-world scenarios, we demonstrate several interesting observations about the success of generative models including diffusion and generative architectures. The efficacy of these models is not only observed for face recognition but also for the improvement of gender classification performance. To the best of our knowledge mitigation of illicit drug abuse is not explored in the literature and hence, this research contributes to enhancing the reliability of face recognition systems amidst the challenges posed by drug abuse, thus advancing both understanding and application in practical settings.