针对人脸识别中每个人只有小规模训练样本的情况,在基于表示的分类(representation based classification,RBC)方法基础上使用由无关类组成的差异字典。差异字典一般由具有面部姿态变化与表情变化的人脸及其基准脸构成,需要训练样本为...针对人脸识别中每个人只有小规模训练样本的情况,在基于表示的分类(representation based classification,RBC)方法基础上使用由无关类组成的差异字典。差异字典一般由具有面部姿态变化与表情变化的人脸及其基准脸构成,需要训练样本为基准脸才能得到较好的识别效果。为防止小规模训练样本中有非基准脸使差异字典出现识别效果下降的情况,使用灰度对称脸将训练样本中的非基准脸转换为近似基准脸,进行差异字典的训练。实验结果表明,该人脸识别方法在小样本情况下的ORL、GT(Georgia tech)、FERET人脸库上具有良好的表现。展开更多
The nonsymmetry and antipacking pattern representation model （NAM）, inspired by the concept of the packing problem, uses a set of subpatterns to represent an original pattern. The NAM is a promising method for image...The nonsymmetry and antipacking pattern representation model （NAM）, inspired by the concept of the packing problem, uses a set of subpatterns to represent an original pattern. The NAM is a promising method for image representation because of its ability to focus on the interesting subsets of an image. In this paper, we develop a new method for gray-scale image representation based on NAM, called NAM-structured plane decomposition （NAMPD）, in which each subpattern is associated with a rectangular region in the image. The luminance function of pixels in this region is approximated by an oblique plane model. Then, we propose a new and fast edge detection algorithm based on NAMPD. The theoretical analyses and experimental results presented in this paper show that the edge detection algorithm using NAMPD performs faster than the classical ones because it permits the execution of operations on subpatterns instead of pixels.展开更多
文摘针对人脸识别中每个人只有小规模训练样本的情况,在基于表示的分类(representation based classification,RBC)方法基础上使用由无关类组成的差异字典。差异字典一般由具有面部姿态变化与表情变化的人脸及其基准脸构成,需要训练样本为基准脸才能得到较好的识别效果。为防止小规模训练样本中有非基准脸使差异字典出现识别效果下降的情况,使用灰度对称脸将训练样本中的非基准脸转换为近似基准脸,进行差异字典的训练。实验结果表明,该人脸识别方法在小样本情况下的ORL、GT(Georgia tech)、FERET人脸库上具有良好的表现。
基金Supported by the National High Technology Research and Development Program of China (No. 2006AA04Z211)
文摘The nonsymmetry and antipacking pattern representation model （NAM）, inspired by the concept of the packing problem, uses a set of subpatterns to represent an original pattern. The NAM is a promising method for image representation because of its ability to focus on the interesting subsets of an image. In this paper, we develop a new method for gray-scale image representation based on NAM, called NAM-structured plane decomposition （NAMPD）, in which each subpattern is associated with a rectangular region in the image. The luminance function of pixels in this region is approximated by an oblique plane model. Then, we propose a new and fast edge detection algorithm based on NAMPD. The theoretical analyses and experimental results presented in this paper show that the edge detection algorithm using NAMPD performs faster than the classical ones because it permits the execution of operations on subpatterns instead of pixels.
