Robust transgender face recognition: Approach based on appearance and therapy factors
Vijay Kumar, Raghavendra Ramachandra, Anoop Namboodiri, Christoph Busch · 2016
Transgender face recognition is gaining increasing attention in the face recognition community because of its potential in real life applications. Despite extensive progress in traditional face recognition domain, it is very challenging to recognize faces under transgender setting. The gender transformation results in significant face variations, both in shape and texture gradually over time. This introduces additional complexities to existing face recognition algorithms to achieve a reliable performance. In this paper, we present a novel framework that incorporates appearance factor and a transformation factor caused due to Hormone Replacement Therapy (HRT) for recognition. To this extent, we employ the Hidden Factor Analysis (HFA) to jointly model a face under therapy as a linear combination of appearance and transformation factors. This is based on the intuition that the appearance factor captures the features that are unaffected by the therapy and transformation factor captures the feature changes due to therapy. Extensive experiments carried out on publicly available HRT transgender face database shows the efficacy of the proposed scheme with a recognition accuracy of 82.36%.