融合AAM、CNN与LBP特征的人脸表情识别方法
发布时间：2018-08-01 11:05
【摘要】：提出一种人脸表情识别方法,融合主动形状模型(AAM)、卷积神经网络(CNN)和局部二元模式(LBP)3种特征区分不同表情。进行图像预处理操作,核心是采用AAM方法进行姿态校正与面部裁剪,得到规范化的表情图像,在全图上提取AAM和CNN两组全局特征,在AAM定位的6个人脸局部区域图像上提取LBP局部特征,融合全局特征和局部特征,采用随机森林方法进行特征分类。在Cohn-Kanade数据集上的实验结果表明,该方法的表情识别率高,是一种有效的表情识别方法。
[Abstract]:A facial expression recognition method is proposed, which combines the active shape model (AAM),) convolution neural network (CNN) and the local binary pattern (LBP) to distinguish different facial expressions. The core of image preprocessing is to use AAM method for attitude correction and facial clipping, to get standardized facial expression image, and to extract two groups of global features of AAM and CNN on the whole image. The local features of LBP are extracted from the local images of 6 faces located by AAM, and the global features and local features are fused, and the feature classification is carried out by using the stochastic forest method. The experimental results on the Cohn-Kanade dataset show that this method has a high expression recognition rate and is an effective expression recognition method.
【作者单位】： 河南工程学院计算机学院;郑州大学软件与应用技术学院;
【基金】：国家社会科学基金项目(15XTQ010)
【分类号】：TP391.41
[Abstract]:A facial expression recognition method is proposed, which combines the active shape model (AAM),) convolution neural network (CNN) and the local binary pattern (LBP) to distinguish different facial expressions. The core of image preprocessing is to use AAM method for attitude correction and facial clipping, to get standardized facial expression image, and to extract two groups of global features of AAM and CNN on the whole image. The local features of LBP are extracted from the local images of 6 faces located by AAM, and the global features and local features are fused, and the feature classification is carried out by using the stochastic forest method. The experimental results on the Cohn-Kanade dataset show that this method has a high expression recognition rate and is an effective expression recognition method.
【作者单位】： 河南工程学院计算机学院;郑州大学软件与应用技术学院;
【基金】：国家社会科学基金项目(15XTQ010)
【分类号】：TP391.41
【相似文献】
相关期刊论文 前10条
1 王志良,陈锋军,薛为民;人脸表情识别方法综述[J];计算机应用与软件;2003年12期
2 孙蔚;王波;;人脸表情识别综述[J];电脑知识与技术;2012年01期
3 杨梅娟;;人脸表情识别综述[J];甘肃科技;2006年04期
4 刘晓e，
本文编号：2157368
本文链接：https://www.wllwen.com/kejilunwen/ruanjiangongchenglunwen/2157368.html
