Enhancing Fingerprint Security Using CNN for Robust Biometric Authentication and Spoof Detection

Abstract

Fake fingerprint recognition needs to pose a significant problem in the quickly developing field of biometric security. The creation of creative and effective spoof detection systems has become crucial as weaknesses in traditional authentication methods have been revealed. To solve this problem, this research employs Convolutional Neural Networks (CNNs) for next-generation biometric security, a ground-breaking approach. The proposed CNN-based fingerprint spoof detection approach utilizes deep learning inherent capabilities to extract complex patterns and information from fingerprint photos. The CNN model trains on a broad dataset containing multiple spoofing attempts to distinguish precisely between authentic and fake fingerprints. There are two significant contributions from this study. First, in order to fully train and assess the CNN model, we provide two datasets covering a wide range of fingerprint spoofing scenarios. Second, by successfully adjusting to the always-changing environment of spoofing tactics, our custom-built architecture outperforms traditional approaches and establishes new standards in detection accuracy. These results highlight CNNs' better capacity to discriminate between authentic and false fingerprints, placing them as a key component of future biometric security systems. This study highlights the value of utilizing cutting-edge technologies, like CNNs, to strengthen biometric security against new threats. By combining cutting-edge deep learning algorithms with the complexities of fingerprint spoof detection, we enter a new age of next-generation biometric security that promises enhanced accuracy and resilience in the face of shifting challenges. Comparing the suggested system to earlier methods, accuracy is 98.99%.