Lian Hui-Cheng, Lu Bao-Liang
Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Rd., Shanghai, 200240, China.
Int J Neural Syst. 2007 Dec;17(6):479-87. doi: 10.1142/S0129065707001317.
In this paper, we present a novel method for multi-view gender classification considering both shape and texture information to represent facial images. The face area is divided into small regions from which local binary pattern (LBP) histograms are extracted and concatenated into a single vector efficiently representing a facial image. Following the idea of local binary pattern, we propose a new feature extraction approach called multi-resolution LBP, which can retain both fine and coarse local micro-patterns and spatial information of facial images. The classification tasks in this work are performed by support vector machines (SVMs). The experiments clearly show the superiority of the proposed method over both support gray faces and support Gabor faces on the CAS-PEAL face database. A higher correct classification rate of 96.56% and a higher cross validation average accuracy of 95.78% have been obtained. In addition, the simplicity of the proposed method leads to very fast feature extraction, and the regional histograms and fine-to-coarse description of facial images allow for multi-view gender classification.
在本文中，我们提出了一种新颖的多视图性别分类方法，该方法同时考虑形状和纹理信息来表示面部图像。面部区域被划分为多个小区域，从中提取局部二值模式（LBP）直方图，并将其有效地连接成一个单一向量，以表示面部图像。遵循局部二值模式的思想，我们提出了一种新的特征提取方法，称为多分辨率LBP，它可以保留面部图像的精细和粗糙局部微模式以及空间信息。这项工作中的分类任务由支持向量机（SVM）执行。实验清楚地表明，在CAS-PEAL面部数据库上，所提出的方法优于支持灰度面部和支持Gabor面部的方法。获得了96.56%的更高正确分类率和95.78%的更高交叉验证平均准确率。此外，所提出方法的简单性导致特征提取非常快速，并且面部图像的区域直方图和从精细到粗糙的描述允许进行多视图性别分类。
