鲁棒的正则化编码随机遮挡表情识别

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 re-gression 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 oc-cluded 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 re-sidual 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.