This paper introduces a novel face recognition problem domain: the medically altered face for gender transformation. A data set of >1.2 million face images was constructed from wild videos obtained from You Tube of 38 subjects undergoing hormone replacement therapy (HRT) for gender transformation over a period of several months to three years. The HRT achieves gender transformation by severely altering the balance of sex hormones, which causes changes in the physical appearance of the face and body. This paper explores that the impact of face changes due to hormone manipulation and its ability to disguise the face and hence, its ability to effect match rates. Face disguise is achieved organically as hormone manipulation causes pathological changes to the body resulting in a modification of face appearance. This paper analyzes and evaluates face components versus full face algorithms in an attempt to identify regions of the face that are resilient to the HRT process. The experiments reveal that periocular face components using simple texture-based face matchers, local binary patterns, histogram of gradients, and patch-based local binary patterns out performs matching against the full face. Furthermore, the experiments reveal that a fusion of the periocular using one of the simple texture-based approaches (patched-based local binary patterns) out performs two Commercial Off The Shelf Systems full face systems: 1) PittPatt SDK and 2) Cognetic FaceVACs v8.5. The evaluated periocular-fused patch-based face matcher outperforms PittPatt SDK v5.2.2 by 76.83% and Cognetic FaceVACS v8.5 by 56.23% for rank-1 accuracy.