Multimodal Biometric Authentication System Using Local Hand Features

In this work, the hand-based multimodal biometric system is presented using score-level fusion of hand geometry and local palmprint features. Initially, a palm ROI of fixed size has been cropped on the basis of finger base points. However, these images are not well aligned and reduce the matching accuracy. To better align them, L-K tracking-based palm image alignment method has been presented. Following this, the poor contrast ROI image is enhanced using novel fractional G-L filter. Then, local keypoints of aligned ROI images are extracted using Block–SIFT descriptor. Secondly, a set of novel geometrical features has been computed from Palmer region of hand image. Further, the highly uncorrelated features are selected from palm and hand geometry using Dia-FLD. In order to handle robust classification, a high-performance method Linear SVM has been used. Finally, score-level fusion rule has been employed which has shown the increased performance of combined approach in terms of Correct Recognition Rate (99.34%), Equal Error Rate (2.16%), and Computation Time (2084 ms). The proposed system has been tested on largest publicly available contact based and contactless databases: Bosphorus hand database, CASIA, and IITD palmprint databases to validate the results.