Design and Development of Novel Artificial Intelligence Assisted Children's Fingerprint Recognition System using Enhanced Biometrical Strategy

• Published in: 2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) pp. 1 - 8
• Main Authors: Simon, Judy, Kapileswar, N, B, Padmavathi, Murthy, Aharam Ramanjani, Sreeja, Gudimella, N, Jayaprashanth
• Format: Conference Proceeding
• Language: English
• Published: IEEE 14.12.2023
• Subjects: artificial intelligence (AI); Artificial neural networks; Authentication; Computer architecture; Fingerprint; Fingerprint recognition; Medical services; Novel Artificial Intelligence Assisted Children's (NAIAC); Pediatrics; Privacy
• DOI: 10.1109/ICSES60034.2023.10465523

The Novel Artificial Intelligence Assisted Children's (NAIAC) Fingerprint Recognition System using Modified Artificial Neural Network presents an innovative approach to biometric authentication tailored specifically for children. In an era marked by heightened concerns for child security and privacy, this research introduces a pioneering solution that harnesses the potential of artificial intelligence (AI) and a modified neural network architecture to achieve accurate and dependable identification of young individuals based on their fingerprints. This system effectively addresses the unique challenges associated with fingerprint recognition in children, such as evolving physical features and limited fingerprint data. Through the application of a specialized artificial neural network architecture, enhanced with modifications tailored to the intricacies of children's fingerprints, NAIAC achieves exceptional accuracy and robustness in the identification and authentication of young subjects. Notable features of the NAIAC system encompass its adaptability to changing physical traits, remarkable reduction in false-positive rates, and seamless integration into a wide range of applications, including child protection, access control in educational institutions, and healthcare management. Furthermore, the NAIAC system places paramount importance on adhering to stringent privacy regulations and ethical guidelines, ensuring that children's sensitive biometric data remains secure and confidential. An accuracy of 99.12% is considered exceptionally high in many applications. It indicates that the model is making accurate predictions or classifications for a vast majority of cases, which is often the primary objective in machine learning.