| Summary: | 碩士 === 國立臺灣大學 === 工程科學及海洋工程學研究所 === 107 === With the increasing requirements for face recognition in many authentication systems, how to prevent intruders from accessing the permission via Face Anti-Spoofing（FAS） techniques has become an important research area in biometrics. After the endeavors over the past few years, researchers around the world have achieved acceptable FAS detection accuracy in the same training and testing dataset. However, it is still problematic when the model trained on one dataset is tested on some other datasets. The detection error rate increases dramatically when this kind of cross-dataset evaluation arises. To address this issue, this thesis introduces the unique techniques of transfer learning and unsupervised learning to increase the generalization ability for cross-dataset evaluation. Specifically, we develop a pre-trained deep learning model to extract the high dimension features of the attack and bona fide images, and the extracted features are clustered into two subsets after the dimension is reduced. One particular characteristic of this strategy is that the dataset that being used to train the pre-trained model is not necessarily in the FAS domain, which makes our framework naturally cross-data oriented. This is quite different from other existing transfer learning methods, which mostly utilize the labeled data of the target domain to fine-tune the model parameters. Based on benchmark dataset experiments, our FAS classifier achieved lower average classification error rate (ACER) scores than state-of-the-art methods by 3%. We believe that the proposed semi-supervised learning model is of potential to overcome this challenging FAS task in biometrics.
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