针对传统人脸识别方法在单样本条件下受姿态、表情、遮挡和光照影响识别效果不佳等问题,提出一种改进的纹理特征和边缘特征相结合的人脸描述算子ε-WLBD(ε-Weber Local Binary Descriptor)。先用改进的局部二值模式和改进的Kirsch算子...针对传统人脸识别方法在单样本条件下受姿态、表情、遮挡和光照影响识别效果不佳等问题,提出一种改进的纹理特征和边缘特征相结合的人脸描述算子ε-WLBD(ε-Weber Local Binary Descriptor)。先用改进的局部二值模式和改进的Kirsch算子进行纹理特征和边缘特征提取,然后分别进行直方图统计,并将其串接起来作为人脸识别的总体特征向量,最后利用最近邻算法进行分类识别。在YALE和AR人脸库上进行测试,实验结果表明所提方法简单有效,且对姿态、表情、遮挡和光照等变化具有较强鲁棒性,对单样本人脸描述具有较好的效果。展开更多
A new algorithm taking the spatial context of local features into account by utilizing contextualized histograms was proposed to recognize facial expression. The contextualized histograms were extracted fromtwo widely...A new algorithm taking the spatial context of local features into account by utilizing contextualized histograms was proposed to recognize facial expression. The contextualized histograms were extracted fromtwo widely used descriptors—the local binary pattern（ LBP） and weber local descriptor（ WLD）. The LBP and WLD feature histograms were extracted separately fromeach facial image,and contextualized histogram was generated as feature vectors to feed the classifier. In addition,the human face was divided into sub-blocks and each sub-block was assigned different weights by their different contributions to the intensity of facial expressions to improve the recognition rate. With the support vector machine（SVM） as classifier,the experimental results on the 2D texture images fromthe 3D-BU FE dataset indicated that contextualized histograms improved facial expression recognition performance when local features were employed.展开更多
文摘针对传统人脸识别方法在单样本条件下受姿态、表情、遮挡和光照影响识别效果不佳等问题,提出一种改进的纹理特征和边缘特征相结合的人脸描述算子ε-WLBD(ε-Weber Local Binary Descriptor)。先用改进的局部二值模式和改进的Kirsch算子进行纹理特征和边缘特征提取,然后分别进行直方图统计,并将其串接起来作为人脸识别的总体特征向量,最后利用最近邻算法进行分类识别。在YALE和AR人脸库上进行测试,实验结果表明所提方法简单有效,且对姿态、表情、遮挡和光照等变化具有较强鲁棒性,对单样本人脸描述具有较好的效果。
基金Supported by the National Natural Science Foundation of China(60772066)
文摘A new algorithm taking the spatial context of local features into account by utilizing contextualized histograms was proposed to recognize facial expression. The contextualized histograms were extracted fromtwo widely used descriptors—the local binary pattern（ LBP） and weber local descriptor（ WLD）. The LBP and WLD feature histograms were extracted separately fromeach facial image,and contextualized histogram was generated as feature vectors to feed the classifier. In addition,the human face was divided into sub-blocks and each sub-block was assigned different weights by their different contributions to the intensity of facial expressions to improve the recognition rate. With the support vector machine（SVM） as classifier,the experimental results on the 2D texture images fromthe 3D-BU FE dataset indicated that contextualized histograms improved facial expression recognition performance when local features were employed.
文摘传统的检测方法存在识别率低、特征维度高的缺点,为此对特征维度与拼接检测精度之间的关系进行分析,基于以像素为基础的改进特征维度的思想,提出一种信息熵与差分激励融合的图像拼接检测方法。提取图像信息熵和韦伯局部特征中(Weber local descriptor,WLD)的差分激励,采用差分直方图进行特征融合,使用v-SVM(v-support vector machine)分类器建立模型,判定图像是否经过拼接。实验结果表明,在哥伦比亚图片库拼接检测中,相比传统的马尔科夫特征、韦伯局部特征等算法,该方法的检测结果具有特征维度低、检测精度高、应用范围广的优点,为快速拼接检测提供了依据。
