Enhanced Multimodal Biometric Recognition Based upon Intrinsic Hand Biometrics

In the proposed study, we examined a multimodal biometric system having the utmost capability against spoof attacks. An enhanced anti-spoof capability is successfully demonstrated by choosing hand-related intrinsic modalities. In the proposed system, pulse response, hand geometry, and finger-vein biometrics are the three modalities of focus. The three modalities are combined using a fuzzy rule-based system that provides an accuracy of 92% on near-infrared (NIR) images. Besides that, we propose a new NIR hand images dataset containing a total of 111,000 images. In this research, hand geometry is treated as an intrinsic biometric modality by employing near-infrared imaging for human hands to locate the interphalangeal joints of human fingers. The L2 norm is calculated using the centroid of four pixel clusters obtained from the finger joint locations. This method produced an accuracy of 86% on the new NIR image dataset. We also propose finger-vein biometric identification using convolutional neural networks (CNNs). The CNN provided 90% accuracy on the new NIR image dataset. Moreover, we propose a robust system known as the pulse response biometric against spoof attacks involving fake or artificial human hands. The pulse response system identifies a live human body by applying a specific frequency pulse on the human hand. About 99% of the frequency response samples obtained from the human and non-human subjects were correctly classified by the pulse response biometric. Finally, we propose to combine all three modalities using the fuzzy inference system on the confidence score level, yielding 92% accuracy on the new near-infrared hand images dataset.