| Summary: | 碩士 === 國立臺灣科技大學 === 資訊工程系 === 107 === With the development of science and technology, face recognition is now an important technology for authentication in various access control applications, especially used in mobile devices. Unlocking by face has gradually replaced fingerprint identification in some scenarios, which becomes one of the major biometric authentication technology of mobile phones.
In a common camera, due to the lack of depth information, it is easy to make fake face images to crack the identification system (e.g., paper printing and screen display) compared with other biological features such as fingerprints and palm prints. Therefore, face liveness detection against spoofing attack using 2D image distortion analysis will be a very important issue in the field of information security.
By virtue of the different features between real faces and fake faces, this thesis adopts local binary pattern and 2D image distortion analysis to extract texture information of images, which are used for developing our face liveness detection system against spoofing attack to distinguish fake faces from real faces by a deep neural network. The system employs only a single image captured from a common camera to discriminant real faces and fake faces.
In the experiments, three kinds of face spoofing databases are used as subjects of cross-validation. The methods and dataset made by ourselves presented in this thesis can effectively classify the authenticity of human faces. The accuracy of the inside test reaches 99.55%, while that of the outside test attains 95.13%. The experimental results show that our face liveness detection system has high accuracy and generality.
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