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Biometrics emerged as a robust solution for security systems. However, giventhe dissemination of biometric applications, criminals are developingtechniques to circumvent them by simulating physical or behavioral traits oflegal users (spoofing attacks). Despite face being a promising characteristicdue to its universality, acceptability and presence of cameras almosteverywhere, face recognition systems are extremely vulnerable to such fraudssince they can be easily fooled with common printed facial photographs.State-of-the-art approaches, based on Convolutional Neural Networks (CNNs),present good results in face spoofing detection. However, these methods do notconsider the importance of learning deep local features from each facialregion, even though it is known from face recognition that each facial regionpresents different visual aspects, which can also be exploited for facespoofing detection. In this work we propose a novel CNN architecture trained intwo steps for such task. Initially, each part of the neural network learnsfeatures from a given facial region. Afterwards, the whole model is fine-tunedon the whole facial images. Results show that such pre-training step allows theCNN to learn different local spoofing cues, improving the performance and theconvergence speed of the final model, outperforming the state-of-the-artapproaches.

On the Learning of Deep Local Features for Robust Face Spoofing Detection