CAJ | 학술논문

对在光照和人脸表情发生各种变化的环境下，用Coiflet小波包和Radon变换两种简单算法从一些灰度级图像数据库中进行人脸识别．这两种算法都能将二维灰度级的人脸图像培训集转换成各自的深度图或物理形态，随后转化为Coiflet小波包和Radon变换，用于特征提取的计算．实验表明，这种转换过来的形态特征对光照和人脸表情的变化具有鲁棒性．
대재광조화인검표정발생각충변화적배경하，용Coiflet소파포화Radon변환량충간단산법종일사회도급도상수거고중진행인검식별．저량충산법도능장이유회도급적인검도상배훈집전환성각자적심도도혹물리형태，수후전화위Coiflet소파포화Radon변환，용우특정제취적계산．실험표명，저충전환과래적형태특정대광조화인검표정적변화구유로봉성．
The effectiveness of two simple algorithms with the use of coiflet packet and Radon transforms respec tively to recognize human faces from some databases of still gray level images under the environment of illumina tion and pose variations is examined. Both the algorithms convert 2-D gray level training face images into their respective depth maps or physical shape which are subsequently transformed into Coiflet packet and Radon trans forms to compute energy for feature extraction. It is shown in the experiments that the transformed shape features are robust to illumination and pose variations.