摘要
为了提高随机遮挡下人脸表情的识别率,提出一种新的人脸表示模型,即鲁棒的正则化编码,通过正则回归系数对给定信号进行鲁棒回归。首先,为了减少遮挡对人脸表情识别系统的影响,待识别表情图像的每个像素点将被分配不同的权重;然后,由于被遮挡部分像素点应分配较小的值,通过连续迭代直到权重收敛于设定的权重阈值;最后,待测图像的稀疏表示将通过最优权重矩阵计算,且待测表情图像分类结果由训练样本逼近待测图像的最小残差决定。应用该方法在日本的JAFFE表情数据库和Cohn-Kanade数据库上取得较理想的结果,且实验结果表明该方法对随机遮挡表情识别具有鲁棒性。
In order to improve facial expression recognition rate under the random shielding,a new face representation model was proposed:robust regularized coding.Regularized regression coefficients are used for carrying out robust regression for the given signals.Firstly,in order to reduce the influence of shielding on facial expression identification system,all pixels of the expression image to be identified will be assigned with different weights;then,because the occluded pixels should have lower weight values,hence,successive iteration is applied until the weight converges to the set weight threshold;finally,the sparse representation of image to be tested can be calculated by using the optimal weight matrix,in addition,the classified results of the expression image to be tested are determined by the minimal residual that the training samples approximate to the test image.The proposed method achieved an ideal performance in Japanese JAFFE expression database and Cohn-Kanade database,in addition,the experimental results show that the method is robust for the recognition of the facial expression randomly shielded.
作者
刘帅师
郭文燕
张言
程曦
LIU Shuaishi;GUO Wenyan;ZHANG Yan;CHENG Xi(College of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130000, China)
出处
《智能系统学报》
CSCD
北大核心
2018年第2期261-268,共8页
CAAI Transactions on Intelligent Systems
基金
吉林省教育厅"十三五"科学技术项目(JJKH20170571KJ)
关键词
随机遮挡
正则化编码
自动更新权重
表情识别
random shielding
regularized coding
automatic update of weight
recognition of facial expression
