CyLab Biometrics Center and the Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
IEEE Trans Image Process. 2013 Aug;22(8):3097-107. doi: 10.1109/TIP.2013.2259835.
Robust facial hair detection and segmentation is a highly valued soft biometric attribute for carrying out forensic facial analysis. In this paper, we propose a novel and fully automatic system, called SparCLeS, for beard/moustache detection and segmentation in challenging facial images. SparCLeS uses the multiscale self-quotient (MSQ) algorithm to preprocess facial images and deal with illumination variation. Histogram of oriented gradients (HOG) features are extracted from the preprocessed images and a dynamic sparse classifier is built using these features to classify a facial region as either containing skin or facial hair. A level set based approach, which makes use of the advantages of both global and local information, is then used to segment the regions of a face containing facial hair. Experimental results demonstrate the effectiveness of our proposed system in detecting and segmenting facial hair regions in images drawn from three databases, i.e., the NIST Multiple Biometric Grand Challenge (MBGC) still face database, the NIST Color Facial Recognition Technology FERET database, and the Labeled Faces in the Wild (LFW) database.
稳健的面部毛发检测和分割是进行法医面部分析的一项非常有价值的软生物特征。在本文中，我们提出了一种名为 SparCLeS 的新颖且全自动的系统，用于在具有挑战性的面部图像中检测和分割胡须/髭。 SparCLeS 使用多尺度自商（MSQ）算法预处理面部图像并处理光照变化。从预处理后的图像中提取方向梯度直方图（HOG）特征，并使用这些特征构建动态稀疏分类器，以将面部区域分类为包含皮肤或面部毛发。然后使用基于水平集的方法，该方法利用全局和局部信息的优势，对面部包含毛发的区域进行分割。实验结果表明，我们提出的系统在从三个数据库（即 NIST 多生物特征大挑战（MBGC）静止面部数据库，NIST 彩色面部识别技术 FERET 数据库和野外标记人脸（LFW）数据库）中检测和分割面部毛发区域方面是有效的。
