Efficient person authentication based on multi-level fusion of ear scores

A two-stage geometric approach that is both scale and rotation invariant is implemented for extracting the unique features present in the surface of an ear image. As occlusion because of ear rings and hair significantly affect the efficiency of ear recognition process, only the middle portion of the ear is considered in this work. The resultant matching scores are compared against a threshold to make a decision for authenticating a person. It is found that the fused scores obtained from the two levels of feature extraction enhance the recognition accuracy compared with that of the individual stages. Finally, particle swarm optimisation technique is applied on the matching scores in order to optimise the fusion parameters such as decision threshold and weights. It results in further improved verification rates compared with the fusion of scores without optimisation. Thus, the proposed method works on partial ear images and demonstrate the presence of more unique features in the middle part of the ear (as seen by the increase in recognition accuracy) and the method also aids in reducing the computation time.