Is the eye region more reliable than the face? A preliminary study of face-based recognition on a transgender dataset

In this work we investigate a truly novel and extremely unique biometric problem: face-based recognition for transgender persons. A transgender person is someone who under goes a gender transformation via hormone replacement therapy; that is, a male becomes a female by suppressing natural testosterone production and exogenously increasing estrogen. Transgender hormone replacement therapy causes physical changes in the body and face. This work provides a preliminary investigation into the effects of these changes on face recognition systems: commercial matcher as well as established texture-based matchers (LBP, HOG, SIFT). The performance of the full-face matchers are compared with that of the periocular region for the same feature sets. The results indicate that periocu-lar recognition out performs the full face on the transgender dataset under real world conditions. In addition, we introduce a novel dataset for researchers: transgender dataset, which was organized from public sources.