  Biometrics is indispensable in this modern digital era for 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 inter-finger posture
variation, satisfactory performances cannot be achieved while the thumb is
included in the contact-free environment. To overcome the hindrances 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, selection of salient features is essential to reduce the
feature dimensionality by eliminating the insignificant features. Weights are
assigned according to the discriminative efficiency of the features to
emphasize on 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
performances are evaluated using the weighted k-nearest neighbor (wk-NN) and
random forest (RF) 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 (EER) of 4.6%
have been achieved using the subset of 25 features which are selected by the
rank-based local FoBa algorithm.
