PUG-FB : Person-verification using geometric and Haralick features of footprint biometric

Abstract

This article demonstrates a study of biometric identification and verification system using foot geometry features. A footprint has three types of features which are sufficient to recognize a person uniquely. These features are categorized into geometric, texture, and minutiae. We have computed most widely used geometry features of the foot using length, width, area, major axis, and minor axis, to identify a person uniquely. Different variations of these features are computed by assigning weights to each feature emphasizing its importance. We have extracted the best variations among foot descriptors, and conclude that the province is the most contributing factor to identify a person foot uniquely. Foot contour features are further combined with foot descriptors to increase the accuracy. For texture, Gray level co-occurrence matrix based on Haralick features is computed with Support Vector Machine as the classifier. Foot biometrics can be used as an additional covert authentication measure where people remove shoes, such as holy places, airport security, swimming pools, wellness centers etc. It can also be used for newborn authentication and identification in hospitals. The method achieves GenuineAcceptRate(GAR) of 82% with the FalseAcceptRate(FAR) of 2.0%, and GAR of 85% with the FAR of 4.0% in case of combination sum rule. GenuineAcceptRate(GAR) has increased to 87.5% at FalseAcceptRate(FAR) of 2.0% including texture features as Gray level co-occurrence matrix.