Face recognition technology has been widely used due to the convenience it provides. However, face recognition is vulnerable to spoofing attacks which limits its usage in sensitive application areas. This work introduces a novel face anti-spoofing system, FASS, that fuses results of two classifiers. One, random forest, uses the identified by us seven no-reference image quality features derived from face images and its results are fused with a deep learning classifier results that uses entire face images as input. Extensive experiments were performed to compare FASS with state-of-the-art anti-spoofing systems on five benchmark datasets: Replay-Attack, CASIA-MFSD, MSU-MFSD, OULU-NPU and SiW. The results show that FASS outperforms all face anti-spoofing systems based on image quality features and is also more accurate than many of the state-of-the-art systems based on deep learning.

Deep convolutional neural networks are often used for image verification but require large amounts of labeled training data, which are not always available. To address this problem, an unsupervised deep learning face verification system, called UFace, is proposed here. It starts by selecting from large unlabeled data the k most similar and k most dissimilar images to a given face image and uses them for training. UFace is implemented using methods of the autoencoder and Siamese network; the latter is used in all comparisons as its performance is better. Unlike in typical deep neural network training, UFace computes the loss function k times for similar images and k times for dissimilar images for each input image. UFace’s performance is evaluated using four benchmark face verification datasets: Labeled Faces in the Wild (LFW), YouTube Faces (YTF), Cross-age LFW (CALFW) and Celebrities in Frontal Profile in the Wild (CFP-FP). UFace with the Siamese network achieved accuracies of 99.40%, 96.04%, 95.12% and 97.89%, respectively, on the four datasets. These results are comparable with the state-of-the-art methods, such as ArcFace, GroupFace and MegaFace. The biggest advantage of UFace is that it uses much less training data and does not require labeled data.