The morphology of the human ear presents rich and stable information embedded on the curved 3-D surface and has as a result attracted considerable attention from forensic scientists and engineers as a biometric recognition modality. However, recognizing a person’s identity from the morphology of the human ear in unconstrained environments, with insufficient and incomplete training data, strong person-specificity, and high within-range variance, can be very challenging. Following our previous work on ear recognition based on local texture descriptors, we propose to use anatomical and embryological information about the human ear in order to find the autonomous components and the locations where large interindividual variations can be detected. Embryology is particularly relevant to our approach as it provides information on the possible changes that can be observed in the external structure of the ear. We experimented with three publicly available databases, namely: IIT Delhi-1, IIT Delhi-2, and USTB-1, consisting of several ear benchmarks acquired under varying conditions and imaging qualities. The experiments show excellent results, beyond the state of the art.