Implicit authentication based on ear shape biometrics using smartphone camera during a call

From secret knowledge like password up to physical traits as biometrics, current smartphone authentication systems are deemed inconvenience and difficult for users. Burdens on remembering password as well as privacy issues on stolen or forged biometrics have raised a futuristic idea of authentication systems. New system is hoped being transparent and with very minimum user involvement denoted as implicit authentication system. One of the ways to implicitly authenticate users is by authenticating them via image or video captured using smartphone camera during a call. During call interaction, we implicitly take ear image using front smartphone camera to recognize and authenticate users without them realizing. In this paper, we present a novel approach to ear recognition which considers both shape and texture information to represent ear image. Firstly, all Local Binary Pattern (LBP) are combined after extracted and concatenated into a single histogram. Second, in order to get geometric features, we use the idea of ear location center that is easily adjusted by smartphone user. Then, we combine previous steps to represent ear image as a descriptor. The recognition is performed using a nearest neighbor classifier computed feature space with Euclidean distance as a similarity/dissimilarity measure. Our proposed approach is very easy and simple thereby its simplicity allows very fast feature extraction. We foresee that this experiment is applicable directly on smartphone.