FINGER KNUCKLES PATTERNS AND FINGERNAILS RECOGNITION FOR PERSONAL IDENTIFICATION BASED ON MULTI-MODEL DEEP LEARNING FEATURES - Chyad, Haitham Salman; Abbes, Tarek (Malaysian Journal of Computer Science, 2025)
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 side 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 the Hands Module (Media Pipe Module). The research paper focuses on the key points that hand components consist of the base knuckle, the major knuckle, the minor knuckle, the thumb knuckle, and the fingernails, which are one of the important biometric features. Specifically, a multi-model deep learning neural network (DLNN) is used to extract distinct features from each model using the DenseNet201 and Inception V3 models. The dorsal finger knuckle and fingernails of ten concatenated fingers are used to recognize all features extracted by these models. 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 Poly U, 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 Inception V3 models are better than the DenseNet201 model on the 11,076K Hands dataset and the ’Poly U 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 ’Poly U HD’ dataset. According to the multi-model deep learning approach proposed in the work, this approach achieved a significant improvement, achieving rank-1 scores of 99.96% compared to previous studies, which plays an important role in knuckle recognition.