基于Bandlet和KW技术的移动应用面部情感识别  

Facial emotion recognition based on Bandlet and KW technology for mobile applications

作　　者：张小华[1] 黄波[1] ZHANG Xiaohua;HUANG Bo(Computer Science and Technology Department,Chengdu Neusoft University,Dujiangyan,Sichuan 611844,China)

机构地区：[1]成都东软学院计算机科学与技术系,四川都江堰611844

出　　处：《计算机工程与应用》2018年第10期213-218,255,共7页Computer Engineering and Applications

基　　金：四川省教育厅科研项目(No.17ZB0008;No.17ZB0007)

摘　　要：由于情感感知移动应用的智能性和用户易接受性,使情感感知移动应用不断增加。由于移动设备的处理能力有限,因此移动设备上的情感识别方法的算法实现应该实时和高效。提出了一个移动应用上的高精度和低计算复杂度的情感识别方法。在该方法中,人脸视频由智能手机的摄像头捕获,从视频中提取一些有代表性的帧,并且用一个人脸检测模块从这些帧中提取人脸区域。脸部区域被Bandlet变换处理,结果子波被划分为互不重叠的子块。计算每个块的局部二进制值模式的直方图,将所有块的直方图关联起来作为描述面部图像的特征集。用KruskalWallis检验从面部图像特征集中选择最具优势的特征,将这些特征送入高斯混合模型分类器中进行情感识别。实验结果表明,该方法在一个合理的时间内实现了高识别精度。Because of the intelligence and user receptivity of mobile applications,emotion aware mobile applications are increasing.Because of the limited processing power of mobile devices,algorithms for emotion recognition on mobile devices should be implemented in real time and efficiently.This paper proposes a method for emotion recognition with high accuracy and low computational complexity in mobile applications.In this method,first,the face video is captured by a smartphone camera.Some representative frames are extracted from the video,and a human face detection module is used to extract face regions from these frames.Then,the face region is processed by Bandlet transform,and the resultant subband is divided into nonoverlapping blocks.Local binary patterns’histograms are calculated for each block,and the histogram of all blocks is connected as a feature set to describe the facial image.Finally,Kruskal-Wallis test is used to select the most dominant features from the facial image feature sets,and these features are fed into the Gauss mixture model classifier for emotion recognition.Experimental results show that the proposed method achieves high recognition accuracy in a reasonable time.

关 键 词：情感识别 移动应用 Bandlet变换 局部二进制值模式 Kruskal-Wallis检验 

分 类 号：TP3-0[自动化与计算机技术—计算机科学与技术]