Finger Biometric Recognition with Feature Selection

Abstract

Biometrics is indispensable in this modern digital era for the secure automated human authentication in various fields of machine learning and pattern recognition. Hand geometry is a promising physiological biometric trait with ample deployed application areas for identity verification. Due to the intricate anatomic foundation of the thumb and substantial interfinger posture variation, satisfactory performances cannot be achieved while the thumb is included in the contact-free environment. To overcome the hindrance associated with the thumb, four-finger-based (excluding the thumb) biometric approaches have been devised. In this chapter, a four-finger-based biometric method has been presented. Again, the selection of salient features is essential to reduce the feature dimensionality by eliminating insignificant features. Weights are assigned according to the discriminative efficiency of the features to emphasize the essential features. Two different strategies, namely, the global and local feature selection methods are adopted based on the adaptive forward-selection and backward-elimination (FoBa) algorithm. The identification performance is evaluated using the weighted k-nearest neighbor and random forest classifiers. The experiments are conducted using the selected feature subsets over the 300 subjects of the Bosphorus hand database. The best identification accuracy of 98.67% and equal error rate of 4.6% have been achieved by using the subset of 25 features those are selected by the rank-based local FoBa algorithm.