On Applicability of Tunable Filter Bank Based Feature for Ear Biometrics: A Study from Constrained to Unconstrained.

In this paper, an overall framework has been presented for person verification using ear biometric which uses tunable filter bank as local feature extractor. The tunable filter bank, based on a half-band polynomial of 14th order, extracts distinct features from ear images maintaining its frequency selectivity property. To advocate the applicability of tunable filter bank on ear biometrics, recognition test has been performed on available constrained databases like AMI, WPUT, IITD and unconstrained database like UERC. Experiments have been conducted applying tunable filter based feature extractor on subparts of the ear. Empirical experiments have been conducted with four and six subdivisions of the ear image. Analyzing the experimental results, it has been found that tunable filter moderately succeeds to distinguish ear features at par with the state-of-the-art features used for ear recognition. Accuracies of 70.58%, 67.01%, 81.98%, and 57.75% have been achieved on AMI, WPUT, IITD, and UERC databases through considering Canberra Distance as underlying measure of separation. The performances indicate that tunable filter is a candidate for recognizing human from ear images.