ENHANCING ATM SECURITY WITH FACE AUTHETICATION USING DEEP LEARNING

ARTIFICIAL INTELLIGENCE,DEEP LEARNING

The proliferation of Automated Teller Machines (ATMs) has revolutionized banking services, providing convenient access to financial transactions. However, the traditional methods of ATM authentication, such as PINs and cards, are susceptible to various security threats, including theft and fraud. In response, biometric authentication has emerged as a promising solution to enhance ATM security. Among biometric modalities, face recognition has garnered significant attention due to its non-intrusive nature and widespread availability of camera-equipped ATMs. This paper presents a novel approach to augment ATM security by integrating a face biometric authentication system with deep learning techniques. The proposed system leverages convolutional neural networks (CNNs) for robust and efficient face recognition. Through extensive experimentation, we demonstrate the effectiveness of deep learning in accurately identifying individuals based on facial features. Moreover, we explore various pre-processing techniques to enhance the performance of the face recognition system, including image normalization, alignment, and quality assessment. Additionally, we investigate the impact of different network architectures and training strategies on the overall authentication accuracy and computational efficiency.

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