F isherface是人脸特征提取中常用的方法,KPCA+LDA能更好地解决非线性问题。本文把模糊技术与KPCA+LDA相结合提出了一种新的特征抽取方法。首先用KPCA进行初次特征提取,然后利用FKNN计算图像对各类别的隶属程度,再在此基础上用LDA进行...F isherface是人脸特征提取中常用的方法,KPCA+LDA能更好地解决非线性问题。本文把模糊技术与KPCA+LDA相结合提出了一种新的特征抽取方法。首先用KPCA进行初次特征提取,然后利用FKNN计算图像对各类别的隶属程度,再在此基础上用LDA进行二次特征提取。在ORL人脸库上的实验结果表明了该方法的有效性。展开更多
Face recognition from video requires dealing with uncertainty both in tracking and recognition. This paper proposed an effective method for face recognition from video. In order to realize simultaneous tracking and re...Face recognition from video requires dealing with uncertainty both in tracking and recognition. This paper proposed an effective method for face recognition from video. In order to realize simultaneous tracking and recognition, fisherface-based recognition is combined with tracking into one model. This model is then embedded into particle filter to perform face recognition from video. In order to improve the robustness of tracking, an expectation maximization (EM) algorithm was adopted to update the appearance model. The experimental results show that the proposed method can perform well in tracking and recognition even in poor conditions such as occlusion and remarkable change in lighting.展开更多
基金National Natural Science Foundation of Chi-na(No.60375008)Shanghai Key Technolo-gies Pre-research Project(No.035115009)
文摘Face recognition from video requires dealing with uncertainty both in tracking and recognition. This paper proposed an effective method for face recognition from video. In order to realize simultaneous tracking and recognition, fisherface-based recognition is combined with tracking into one model. This model is then embedded into particle filter to perform face recognition from video. In order to improve the robustness of tracking, an expectation maximization (EM) algorithm was adopted to update the appearance model. The experimental results show that the proposed method can perform well in tracking and recognition even in poor conditions such as occlusion and remarkable change in lighting.
