Localization Of Skin Features On The Hand And Wrist From Small Image Patches

Skin-based biometrics rely on the distinctiveness of skin patterns across individuals for identification. In this paper, we investigate whether small image patches of the skin can be localized on a user's body, determining not "who?" but instead "where?" Applying techniques from biometrics and computer vision, we introduce a hierarchical classifier that estimates a location from the image texture and refines the estimate with keypoint matching and geometric verification. To evaluate our approach, we collected 10,198 close-up images of 17 hand and wrist locations across 30 participants. Within-person algorithmic experiments demonstrate that an individual's own skin features can be used to localize their skin surface image patches with an F-1 score of 96.5%. As secondary analyses, we assess the effects of training set size and between-person classification. We close with a discussion of the strengths and limitations of our approach and evaluation methods as well as implications for future applications using a wearable camera to support touch-based, location-specific taps and gestures on the surface of the skin.