基於HSV色彩空間中紋理和方向分析之 單一影像臉部欺騙偵測

Abstract

近年來，有許多活體偵測的方法被提出來對抗照片型的欺騙攻擊，透過分析人臉和印刷臉之間的差異。其中的差異表現在人臉表面的反射程度與陰影部分會與印刷臉的表面不同，還有因為印刷臉是由相片再翻拍出來的影像，因此會有二次反射光的影響。此外，HSV色彩空間所呈現的色彩對於人眼來說較為類似，所以我們認為用這個色彩空間的亮度部分所呈現的影像可以被用在我們的研究上，因此我們利用這個亮度影像來進行臉部的紋理特徵分析使用不同尺寸的LPB運算子，還有用Sobel運算元來進行梯度方向的分析，並且取得其中的特徵來偵測拍攝工具前方的人是否為真實的人臉或是印刷臉。在我們的實驗中，我們比較了其他的方法與其他論文所用的方法，從實驗結果可看到我們所提出來的特徵可以有效的提升臉部欺騙偵測的效能。

In the recent years, there are many liveness detection methods proposed to against photograph spoofing through analyzing the fundamental differences between human faces and printed faces. The differences in surface between human faces and printed faces are distinctive specular reflections and shades because a human face is a complex 3D object whereas a printed face can be seen as a planar object. Because an image recaptured from a photograph has the twice reflection, the gradient direction histogram of the image is different. Furthermore, the HSV color space can be more perfectly to deal with some information which human’s eyes cannot get and closer to the perception of humans. Therefore, we present to analyze facial image textures using multi-scale LBP and gradient direction analysis by Sobel operators on the illumination component of HSV color space for detecting whether there is a live person or a printed face in front of the camera. From our experiment results, we can see that the proposed feature can improve the face spoofing detection performance.