Human Identification Using Selected Features From Finger Geometric Profiles

TL;DR: A finger biometric system at an unconstrained environment at the preprocessing stage that decomposes the main hand contour into finger-level shape representation and the rank-based forward–backward greedy algorithm is followed to select relevant features and to enhance classification accuracy.

Abstract: A finger biometric system at an unconstrained environment is presented in this paper. A technique for hand image normalization is implemented at the preprocessing stage that decomposes the main hand contour into finger-level shape representation. This normalization technique follows subtraction of transformed binary image from binary hand contour image to generate the left-side of finger profiles (LSFPs). Then, XOR is applied to LSFP image and hand contour image to produce the right side of finger profiles. During feature extraction, initially, 30 geometric features are computed from every normalized finger. The rank-based forward–backward greedy algorithm is followed to select relevant features and to enhance classification accuracy. Two different subsets of features containing 9 and 12 discriminative features per finger are selected for two separate experimentations those use the ${k}$ -nearest neighbor and the random forest (RF) for classification on the Bosphorus hand database. The experiments with the selected features of four fingers except the thumb have obtained improved performances compared to features extracted from five fingers and also other existing methods evaluated on the Bosphorus database. The best identification accuracies of 96.56% and 95.92% using the RF classifier have been achieved for the right- and left-hand images of 638 subjects, respectively. An equal error rate of 0.078 is obtained for both types of the hand images.