隨著科技的進步，人臉辨識為一種存取控制應用於身份驗證的新興技術。特別是在行動裝置中越來越受到關注，由於智慧型手機的普及以及行動作業系統中人臉解鎖功能的發佈，人臉辨識逐漸取代指紋辨識，而成為手機的另一種生物特徵認證技術。
基於大部分視訊鏡頭的成像缺少有關臉部的深度資訊，且相較獲取指紋、掌紋等其它生物特徵，翻拍人臉影像相對容易(例如：紙張列印，螢幕顯示)，而且成本也較低，僅需要一張合法身分的照片就可以侵入人臉識別系統，所以人臉的二維圖像防偽檢測將會是資訊安全領域中的一個重要研究課題。
針對人臉真實圖像及欺騙的特徵差異，本論文使用了局部二值模式和圖像失真分析來提取圖片的紋理訊息，最後再透過深度神經網路來進行分類，用以建立一個二維圖像防偽檢測之人臉活體識別系統；相較於傳統使用影片的方式，本系統只需要採集單張影像就可以識別真實人臉還是欺騙的照片。
實驗中使用三種人臉偽造資料庫當作交叉測試的對象。本文提出的方法和資料集可以有效地對人臉的真偽進行分類，在資料內部測試準確率高達99.55%，而在資料外部測試準確度也高達95.13%。從實驗結果可以得知，本文所研發的人臉活體識別系統具有較高的準確度和泛用性。

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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