激光与光电子学进展, 2018, 55 (1): 011002, 网络出版: 2018-09-10
融合局部特征与深度置信网络的人脸表情识别 下载: 1101次
Facial Expression Recognition Based on Fusion of Local Features and Deep Belief Network
图像处理 表情识别 特征融合 Log-Gabor特征 二阶梯度方向直方图特征 深度置信网络 image processing expression recognition feature fusion Log-Gabor features second-order histogram of gradient direction featu deep belief network
摘要
针对传统人脸表情识别(FER)方法所提取的表情特征较为单一,同时对于表情分类器的选择存在局限性的问题,提出一种融合局部特征与深度置信网络(DBN)的FER方法。该方法首先从人脸表情图像中切割出眉毛眼睛部位与嘴巴部位这2种包含丰富表情信息的局部表情图像,对其分别提取包含纹理信息的Log-Gabor特征与包含形状信息的二阶梯度方向直方图特征,并将这2种特征相融合,获得更有效的表情特征,然后利用融合后的特征训练DBN模型,并用训练后的DBN模型进行表情识别。利用本文方法在三种表情库上进行实验,识别率可分别达到96.30%、97.39%以及95.73%,表明本文方法可有效提高人脸表情识别率。
Abstract
The traditional facial expression recognition (FER) methods only extract single expression feature. Meanwhile, the choice of expression classifiers has limitations. To solve these problems, we propose a FER method based on the fusion of local features and deep belief network (DBN). Firstly, the eyebrows and eyes part and mouth part with rich expression information are extracted as local expression images. In order to attain more effective expression features, the Log-Gabor features with texture information and second-order histogram of gradient direction features with shape information are extracted and fused from local expression images. DBN model is trained with fusion features. The trained DBN model is used to recognize the facial expression. The experimental results show that the recognition rates of the proposed method on three databases are 96.30%, 97.39% and 95.73%. The proposed method effectively improves the recognition rate of facial expression.
王琳琳, 刘敬浩, 付晓梅. 融合局部特征与深度置信网络的人脸表情识别[J]. 激光与光电子学进展, 2018, 55(1): 011002. Wang Linlin, Liu Jinghao, Fu Xiaomei. Facial Expression Recognition Based on Fusion of Local Features and Deep Belief Network[J]. Laser & Optoelectronics Progress, 2018, 55(1): 011002.