FINGER KNUCKLES PATTERNS AND FINGERNAILS RECOGNITION FOR PERSONAL IDENTIFICATION BASED ON MULTI-MODEL DEEP LEARNING FEATURES

Abstract

Because biometric recognition systems are reliable and distinctive, they are widely used in many different applications. Hand-based person recognition has gained a lot of attention in recent years because of its stability, feature richness, dependability, and increased user acceptance. Although the dorsal of the hand can be quite helpful in personal identification, it does not receive much attention. Finger knuckle and finger nail biometric traits can be obtained from a single dorsal hand scan. This research paper presents an approach for person identification using the dorsal finger knuckle and fingernails. It provides a structure for automatic person identification which includes the segmentation of the detected components with hand images using Hands Module (MediaPipe Module). The research paper focuses on the keypoints hand components consist of the base knuckle, the major knuckle, the minor knuckle, the thumb knuckle, and the fingernails are one of the important biometric features. In particular, the multi-model deep learning neural network (DLNN) is employed to extract distinctive features from each modality. Different similarity metrics are used to compute the matching procedure for every model individually. An evaluation of the proposed approach was performed using datasets consisting of 11,076K hands with left and right hands dorsal, for 190 persons and 4,650 PolyU, often known as Hong Kong Polytechnic University, Contactless with right hand dorsal for 502 persons. The proposed structure was achieved with results indicating that the inceptionV3 models are better than denseNet201 model on the 11,076K Hands dataset and the ’PolyU HD’ dataset. The left-hand results are better than the right results on the 11,076K Hands dataset and the fingernails produce consistently higher identification results than other hand components, with a rank-1 scores of (99.96% and 96.28%) for inceptionV3 model, (98.11% and 93.42%) for denseNet201 model in the 11,076K Hands dataset and with a rank-1 scores of (97.07%) for inceptionV3 model, (94.83%) for denseNet201 model in the ’PolyU HD’ dataset. According to the multi-model deep learning-based approach proposed in the work, the patterns of the dorsal finger knuckle and fingernails play an important role in person recognition.