On Distinctiveness and Symmetry in Ear Biometrics

Previous works show that human ears can be used for identification, gender classification, and kinship verification and have investigated whether there is a symmetry between a person’s ears; however, the symmetry performances have been less than satisfactory. Our paper extends the analysis of gender classification on ear images and analyses bilateral symmetry of human ears, in both cases aiming to determine the ear parts from which recognition is derived. We use model-based analysis and deep learning methods to capitalize on structure and performance, respectively. We consider the rotation of ear images under an affine transformation, by modelling the ear as a flat plane attached to the head. We address the question as to whether it is possible that given an image of one ear, a person can then be recognized from their other ear. Such a symmetry-based strategy could reduce constraints on applications of ear biometrics. We show that it is possible to recognise the gender with a 90.9% success rate and that the ear rim (the upper helix and lobe) dominates performance. To investigate symmetry, we compare one ear with a mirrored version of the other ear and achieve 93.1% CCR, which is the current state-of-the-art, with important regions different from those determined for gender. To extend the analysis we construct two groups of images, one of which contains both ears from the same subject and the other contains two ears from different subjects. The 100% CCR confirms the existence of symmetry between a subject’s ears. By these approaches we show that there is actually a high chance that there exists symmetry between a person’s ears and that it would be prudent for recognition systems to concentrate on the inner ear rather than the outer ear.