基于线性判别深度信念网络的人脸表情识别  

Facial expression recognition based on linear discriminant deep belief network

作　　者：王健 郭敏[1] 肖冰[1] WANG Jian;GUO Min;XIAO Bing(Key Laboratory of Modern Teaching Technology, Ministry of Education,School of Computer Science, Shaanxi Normal University, Xi＇an 710119, Shaanxi, China)

机构地区：[1]现代教学技术教育部重点实验室陕西师范大学计算机科学学院,陕西西安710119

出　　处：《陕西师范大学学报（自然科学版）》2018年第4期28-34,共7页Journal of Shaanxi Normal University：Natural Science Edition

基　　金：国家自然科学基金(61401265)

摘　　要：深度信念网络在人脸表情识别领域表现出很好的性能,但由于其最后一个隐层与分类层之间的初始权值矩阵通常随机生成,这样的权值矩阵不具有判别能力,从而导致经该权值矩阵映射得到的特征不能保证适合于分类任务。为了解决此问题,提出一种新的深度信念网络结构——线性判别深度信念网络,其对传统线性判别分析法进行改进,设计了一个新的类间离散度矩阵,解决了传统线性判别分析法中存在的秩限问题;使用改进的线性判别分析法初始化深度信念网络最后一个隐层和分类层之间的权值矩阵,使网络更适合于分类任务。本文提出的线性判别深度信念网络在JAFFE和Extended Cohn-Kanade人脸表情数据库上分别得到了78.26%和94.48%的识别率。Deep belief network has good performance on facial expressions recognition, but its initial weight matrix between the last hidden layer and the labeled layer is usually generated randomly with less discriminative ability, which leads to that the features mapped from the initial weight matrix cannot guarantee to be suitable for classification tasks. To address the problem, a new architecture of deep belief network is proposed, named linear discriminant deep belief network. First, the traditional linear discriminant analysis is improved by designing a new between scatter matrix, which addresses the rank problem of the traditional linear discriminant analysis. Then, the improved linear discriminant analysis is used to initialize the weight matrix between the last hidden layer and the labeled layer of deep belief network to make sure that the weight matrix is suitable for classification task. In the experiments, our proposed linear diseriminant deep belief network obtains respectively the recognition rates of 78.26% and 94.48% on the JAFFE database and the Extended Cohn Kanade database.

关 键 词：人脸表情识别 受限玻尔兹曼机 深度信念网络 线性判别分析 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]