Banking Security System Using Face and Liveness Detection Using Machine Learning and Image Processing
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Abstract
To provide safer user login experiences on ever-growing online banking services, it will be important to develop new, better forms of secure authentication. Traditional convolutional methods such as password, pin or one-time passwords are vulnerable to common attacks such as phishing and have been known to lose their validity when credentials are leaked. Biometric authentication based on face recognition offers both convenience and non-contact verification; however, traditional systems can be easily spoofed by fake photos, videos, or even 3-D masks. This paper presents an ML-based method to develop a more robust ID verification system via the use of image processing techniques along with deep learning techniques. An initial CNN will be used to extract facial features and recognize faces. In addition to the CNN, we also propose a liveness detection module that would detect the presence of natural behaviors associated with humans i.e., eye blinks, lip movements and small micro-expression changes in facial expressions to determine if the user is a living
being or attempting to spoof the system. We plan to train and validate our model on publically available datasets like ORL, OULU, and CASIA to ensure that it will work under different lighting conditions, pose variations and textures. The system will utilize Python/TensorFlow/OpenCV/Keras and a MySQL DB. We will test this system’s accuracy/latency/resistance to spoofing attacks. Our experimental results show that the proposed system performs much better than other reported systems (over 98% recognition accuracy, very low FAR & very fast) for online/mobile banking applications. Therefore, this study demonstrates that the proposed system is an inexpensive way to improve security in banking infrastructures. Furthermore, due to the modularity of the proposed system, it can be easily integrated into all current banking platforms while complying with regulations related to data protection.
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