CNN-Based Transfer Learning for 3D Knuckle Recognition

Abstract

A trustworthy and secure identity verification system is in great demand nowadays. The automatic recognition of the 3D middle finger knuckle is a new biometric identifier that could offer a precise, practical, and efficient alternative for personal identification. According to earlier studies, deep learning algorithms could be used for biometric identification. However, the accuracy of the current 3D middle finger knuckle recognition model is relatively low. Motivated by this fact, in this study, seven deep learning neural networks have been modified and trained to identify 3D middle finger knuckle patterns using transfer learning. Using the Hong Kong Polytechnic University’s 3D knuckle image dataset, an extensive experiment was performed. Two sessions of data from different camera lenses were used to assess the performance of the suggested deep learning model. The results show that the InceptionV3 method significantly enhanced the recognition of 3D middle finger knuckle patterns with 99.07% accuracy, followed by Xception, NasNetMobile, and DenseNet201 (97.35%, 92.92%, and 92.59%, respectively), which is superior to the current middle finger knuckle recognition model. This accurate, fast, and automatic middle finger knuckle identification will help to be implemented in real-time and small-scale settings like offices, schools, or personal devices like laptops and smartphones, where training is simple.