FACE SPOOFING DETECTION BASED ON DEEP FEATURE EXTRACTION AND INSTANCE-BASED CLASSIFICATION

Face recognition is an important task in smart home security for detecting a face or monitoring a person in a live video and verifying the identity of an authentic user. However, there have been spoofing face methods that can trick a face recognition algorithm into wrongly verifying the identity of the person. In this paper, we propose a new hybrid framework for spoofing face detection based on Convolutional Neural Network and Long Short-Term Memory (CNNLSTM) and instance-based learning algorithm. In addition, a new dataset called FSA-CCTV is proposed, which contains face images from CCTV video clips with many types of spoofing attacks. The performance of our method was compared to several other anti-spoofing methods: CNN and RI-LBP, SLRNN, HSV+YCbCr, ResNet50, YCbCr+SVM and YCbCr+KNN. The experimental results show that our method yielded 93.2% of Accuracy, 96.8% of Recall, 94% of Precision, 94.8% of F1-score and 0.93 of AUC on the FSA-CCTV dataset. From the experimental results we can conclude that the proposed algorithm outperforms other approaches and yielded the most stable classification accuracy on the proposed dataset.