针对人脸深度图像的分类识别问题展开研究,提出一种自适应3DLBP(3D Local Binary Pattern,3DLBP)特征提取算法。该特征提取算法以机器学习理论为基础,首次将反馈学习理论与3DLBP特征提取过程相结合,以保证特征提取算法对训练样本集的变...针对人脸深度图像的分类识别问题展开研究,提出一种自适应3DLBP(3D Local Binary Pattern,3DLBP)特征提取算法。该特征提取算法以机器学习理论为基础,首次将反馈学习理论与3DLBP特征提取过程相结合,以保证特征提取算法对训练样本集的变化具有理想的普适性;同时,为了提高自适应特征提取算法的稳定性,提出使用多分类器对反馈学习过程进行优化。实验结果表明,自适应3DLBP特征对训练样本集的变化具有较好的有效性和稳定性,在FRGCv2.0人脸数据库上取得了理想的识别效果。展开更多
Local binary pattern(LBP)is an important method for texture feature extraction of facial expression.However,it also has the shortcomings of high dimension,slow feature extraction and noeffective local or global featur...Local binary pattern(LBP)is an important method for texture feature extraction of facial expression.However,it also has the shortcomings of high dimension,slow feature extraction and noeffective local or global features extracted.To solve these problems,a facial expression feature extraction method is proposed based on improved LBP.Firstly,LBP is converted into double local binary pattern(DLBP).Then by combining Taylor expansion(TE)with DLBP,DLBP-TE algorithm is obtained.Finally,the DLBP-TE algorithm combined with extreme learning machine(ELM)is applied in seven kinds of ficial expression images and the corresponding experiments are carried out in Japanese adult female facial expression(JAFFE)database.The results show that the proposed method can significantly improve facial expression recognition rate.展开更多
文摘针对人脸深度图像的分类识别问题展开研究,提出一种自适应3DLBP(3D Local Binary Pattern,3DLBP)特征提取算法。该特征提取算法以机器学习理论为基础,首次将反馈学习理论与3DLBP特征提取过程相结合,以保证特征提取算法对训练样本集的变化具有理想的普适性;同时,为了提高自适应特征提取算法的稳定性,提出使用多分类器对反馈学习过程进行优化。实验结果表明,自适应3DLBP特征对训练样本集的变化具有较好的有效性和稳定性,在FRGCv2.0人脸数据库上取得了理想的识别效果。
文摘Local binary pattern(LBP)is an important method for texture feature extraction of facial expression.However,it also has the shortcomings of high dimension,slow feature extraction and noeffective local or global features extracted.To solve these problems,a facial expression feature extraction method is proposed based on improved LBP.Firstly,LBP is converted into double local binary pattern(DLBP).Then by combining Taylor expansion(TE)with DLBP,DLBP-TE algorithm is obtained.Finally,the DLBP-TE algorithm combined with extreme learning machine(ELM)is applied in seven kinds of ficial expression images and the corresponding experiments are carried out in Japanese adult female facial expression(JAFFE)database.The results show that the proposed method can significantly improve facial expression recognition rate.