Banking Security System with Face Liveness Detection Using Machine Learning and Image Processing

The face is a significant part of the human body, recognizing people in enormous gatherings. Subsequently, on account of its all-inclusiveness and uniqueness, it has turned into the most generally utilized and acknowledged biometric strategy. Biometrics with facial recognition is now widely used. A face identification system should identify not only someone's faces but also detect spoofing attempts with printed face or digital presentations. A sincere spoofing prevention approach is to examine face liveness, such as eye blinking and lips movement. Nevertheless, this approach is helpless when dealing with videobased replay attacks. For this reason, this paper proposes a combined method of face liveness detection and CNN (Convolutional Neural Network) classifier. The anti-spoofing method is designed with two modules, the blinking eye module that evaluates eye openness and lip movement, and the CCN classifier module. The dataset for training our CNN classification can be from a variety of publicly available sources. The test results show that the module created can recognize various kinds of facial spoof attacks, such as using posters, masks, or smartphones.