摘要
利用自适应小波变换(AWT)能融合不同母小波的优点,采用盖伯母小波和墨西哥帽母小波构成自适应小波。以光学人脸识别中降噪问题为应用背景,使用神经网络法对小波参数和组合系数进行优化,将生成的多母小波自适应小波滤波器用作人脸特征提取器。对噪声图像做特征提取,进行相关识别,采用3个指标定量分析识别结果。同盖伯小波和墨西哥帽小波识别结果的比较表明,多母小波自适应小波具有不同母小波的优点,并有良好的降噪性能。
Considering the advantage for combining different mother wavelets of adaptive wavelet transform (AWT), six wavelets selected from Gabor wavelets and Mexican-hat wavelet are chosen to construct a multi-mother adaptive wavelet, For the purpose of denoising in optical face pattern recognition,using artificial neural networks,the parameters of the wavelets are optimized, Several criterions are used for evaluating the correlation recognition performance. After comparing the criterions of Gabor wavelets, Mexican-hat wavelet and the new adaptive wavelet, the evaluation shows that the new adaptive wavelet combines advantages of these two mother wavelets and has good de-noising ability, Moreover,the optical filter of this adaptive wavelet can be applied in optical pattern recognition.
出处
《光电子.激光》
EI
CAS
CSCD
北大核心
2005年第11期1359-1362,共4页
Journal of Optoelectronics·Laser
基金
国家自然科学基金资助项目(60277012)
关键词
自适应小波变换(AWT)
多母小波
人脸识别
降噪
adaptive wavelet transform(AWT)
multi-mother wavelets
face recognition
de-noising