Ahonen Timo, Hadid Abdenour, Pietikäinen Matti
Machine Vision Group, Department of Electrical Information Engineering, University of Oulu, Finland.
IEEE Trans Pattern Anal Mach Intell. 2006 Dec;28(12):2037-41. doi: 10.1109/TPAMI.2006.244.
This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced feature vector to be used as a face descriptor. The performance of the proposed method is assessed in the face recognition problem under different challenges. Other applications and several extensions are also discussed.
本文提出了一种基于局部二值模式(LBP)纹理特征的新颖且高效的面部图像表示方法。面部图像被划分为多个区域,从中提取LBP特征分布并连接成一个增强特征向量,用作面部描述符。在不同挑战下的人脸识别问题中评估了所提方法的性能。还讨论了其他应用和几种扩展。