基於雙通道紋理和顏色失真分析之單一影像人臉欺騙偵測

Abstract

近年來，透過分析人臉和2D假人臉之間的差異，有許多假人臉偵測的方法被提出來對抗列印於紙上及顯示於螢幕上之2D假人臉欺騙攻擊，其中的差別處在於真臉以及假臉表面之間存在差異性的陰影與全反射光影，因為真人臉為一個立體物體，而2D假人臉則可視為平坦表面。除此之外，由於產生欺騙攻擊的媒介品質，例如列表機及螢幕，一張假人臉會產生與真臉不同的顏色分佈。舉例來說，一張印刷之假人臉會有較低的顏色對比，而螢幕顯示的假人臉則會有較高的顏色對比。基於以上真臉與假臉之間不同的特性，我們提出一個結合兩種紋理之特徵以及一種顏色特徵的方法來偵測在拍攝工具前是否為真實的人臉。其中所結合的兩種紋理特徵包含多尺寸的LBP，以及本論文新提出的R-G差異紋理。而使用到的顏色特徵則為顏色矩，它是用於量測一張彩色影像顏色分佈的測量值。從實驗結果可看出本文提出的方法的確能夠提升假人臉偵測的效果。

In recent years, there are many spoofing face detection methods proposed to against 2D face spoofing attacks including printed photos and screen displayed photos through analyzing the differences between human faces and fake faces. The difference in light reflection between human faces and 2D fake faces mainly comes from distinctive shades and specular reflections because a human face is a complex 3D object whereas a 2D fake face is a flat surface. Besides, the color distribution of a fake face image is quite different to a real face due to the qualities of spoofing mediums such as a printer and a screen. For example, a printed face image usually has lower color contrast than a real face while a screen displayed face image has higher color contrast. For these different properties, this thesis proposes an approach by using the combination of two texture features and a color feature to determine whether there is a live person or a spoofing face in front of the camera. The two texture features include multi-scale LBP and the R-G deviated texture proposed in this thesis, while the color feature adopts the color moment, which is a measurement of the color distribution of an image. From the experimental results, the proposed approach indeed improves the performance of spoofing face detection.