Abstract
One of the most widely used biometric approaches is face recognition. Face recognition is used in many Fields. One of these fields is mobile devices authentication. While the number of mobile device users increasing year after year, the need for mobile security is also gaining ground. However, face recognition can be easily attacked by a malicious face spoofing. That is intended to deceive the face recognition system by facial pictures obtained from images or videos. Other cheaters show the mask of an authorized person to fool the recognition camera into a real person. Liveness detection is an important research topic to detect face spoofing. The proposed approach in this paper is a deep learning technique which is a sequential CNN (convolution Neural Network) divided into a feature extraction stage and a classification stage. The dataset used is CelebA-Spoof (2020) collected to recognize live and non-live faces. The experiment is performed on a part of the CelebA-Spoof dataset. The performance of the proposed approach is measured in terms of accuracy. The accuracy of testing the system on unseen data is 87% and the area under ROC curve is 0.535. there are many new techniques are intended to be used in future work such as capsule neural networks is expected to improves our results.
Published Version
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