Face Anti-Spoofing Using CNN Classifier & Face liveness Detection
Raden Budiarto Hadiprakoso, Hermawan Setiawan, Girinoto
Abstract
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 video-based 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. We combined these two modules sequentially and implemented them into a simple facial recognition application using the Android platform. The test results show that the module created can recognize various kinds of facial spoof attacks, such as using posters, masks, or smartphones.