NHẬN DIỆN KHUÔN MẶT GIẢ MẠO SỬ DỤNG KỸ THUẬT HỌC SÂU

Abstract

In the modern digital era as present, facial recognition is used more commonly than ever in numerous sectors of technology and science. Compared to other biometric identification techniques such as fingerprints, iris scans, etc., facial recognition systems have reduced many related issues and are more effective for human identification. With the distribution of face recognition, it now gives considerable attention on information security and system security. However, some gaps remain in the “appropriate recognition” that should be addressed, including the  both subjective and objective reasons like weather or even the transmission of the detectors. In this paper, we suggest a deep neural network diagram for anti-face spoofing and liveness detection by adopting a Convolutional Neural Network (CNN) divided into feature extraction and classification stages, in which the dataset used is CelebA Spoof (2020), collected for direct and indirect face recognition. Experiments were conducted on a subset of the CelebA-Spoof dataset. The research experiment showed that the model achieved an average accuracy of 87%. With this result, the study provides a method that can improve the performance of the facial recognition technology.