摘要
提出了一种结合Gabor变换和FastICA技术的人脸表情特征提取方法。Gabor小波具有很好的空频局部性和多方向选择性,因此更有利于表情细节信息的提取。FastICA技术能够消除信号间的高阶统计冗余。对图像进行Gabor变换,把得到的系数排列成Gabor特征矢量,用FastICA对Gabor特征矢量进行特征提取,用K-近邻分类器进行分类。JAFFE表情库中的实验证明该方法的有效性。
An effective method for the facial expression feature extraction is presented by combining the Gabor transform with the Fast Independent Component Analysis（FastICA）.Gabor wavelets exhibit strong characteristics of spatial locality and orienta-tion selectivity,which are good for the extraction of the image’s texture.FastICA can reduce the redundancy of high-order statis-tics.The Gabor transform is carried out on each original image,and the outputs are concatenated into a Gabor feature vector.Fas-tICA approach is used to extract features from the Gabor feature vectors of all the images.The K-neighbor method is used for classification.A series of experiments performed on the JAFFE database indicate the efficiency of the proposed method.
出处
《计算机工程与应用》
CSCD
北大核心
2011年第24期178-181,共4页
Computer Engineering and Applications
关键词
表情识别
特征提取
GABOR变换
快速独立成分分析
facial expression recognition
feature extraction
Gabor transform
fast independent component analysis
