Open AccessDeep ContactlessS 3D-Middle Finger Knuckle Recognition
Author(s) -
Nigar M. Shafiq Surameery
Publication year - 2021
Publication title -
science journal of university of zakho
Language(s) - English
Resource type - Journals
eISSN - 2663-6298
pISSN - 2663-628X
DOI - 10.25271/sjuoz.2021.9.4.854
Subject(s) - knuckle , biometrics , convolutional neural network , computer science , artificial intelligence , field (mathematics) , authentication (law) , artificial neural network , identity (music) , hand geometry , pattern recognition (psychology) , computer vision , engineering , computer security , acoustics , pure mathematics , mechanical engineering , physics , mathematics
Automation security is one of the main concerns of modern times. A safe and reliable identity verification system is in great demand. A biometric verification system can represent a reliable method of identifying an individual. The knuckle pattern is considered to be one of the emerging hand biometrics because it has the potential to identify individuals. This presented work explores the possibility of using 3D middle finger knuckle for biometric identification. The study provides a new simple, trained from scratch but effective deep convolutional neural network model that designed for 3D figure knuckle print recognition. To the best of the author's knowledge, this is the first successful attempt to develop 3D middle finger knuckle based authentication system using Convolutional Neural Network (CNN). Extensive experiment was carried out using (HKPolyU) the 3D knuckle image database of Hong Kong Polytechnic University. The performance of the proposed CNN model has been evaluated using two session data obtained from different camera lenses. The model designed to be implemented in a real-time system. It can be deployed in a small-scale environment like offices, houses, or personal devices, where the training is very easy. The experimental results were very encouraging and showed the potential of using the 3D middle finger knuckle pattern for biometric applications. The results confirmed that despite various challenges compared with other studies in the same field, the proposed method provides an optimistic solution with an accuracy of 71%.