CAJ | 학술논문

针对 Gabor 小波提取人脸曲线脸部特征不够完整的问题,通过增加一个曲率参数,将常规的 Gabor 小波扩展为弧形 Gabor 小波(CGW),以获取更多有效的人脸特征。CGW 在5个尺度和16个方向上提取人脸特征,利用主成分分析(PCA)对人脸特征进行选择和降维,并采用最近邻方法进行分类。在 ORL 数据库中每人选取5张图像作为训练样本,5张图像作为测试样本,并分别与 PCA 和 Gabor 小波方法进行比较。实验结果表明,CGW 具有更好的识别效果。
침대 Gabor 소파제취인검곡선검부특정불구완정적문제,통과증가일개곡솔삼수,장상규적 Gabor 소파확전위호형 Gabor 소파(CGW),이획취경다유효적인검특정。CGW 재5개척도화16개방향상제취인검특정,이용주성분분석(PCA)대인검특정진행선택화강유,병채용최근린방법진행분류。재 ORL 수거고중매인선취5장도상작위훈련양본,5장도상작위측시양본,병분별여 PCA 화 Gabor 소파방법진행비교。실험결과표명,CGW 구유경호적식별효과。
In order to solve the problem of face feature incompletely extracted by Gabor wavelet,a curvature parameter is added to structure curve Gabor wavelet(CGW)for obtaining more effective face features.Face features are taken through CGW on 5 scales and 1 6 directions.Then principal component analysis (PCA)method is used to select the features and re-duce dimensionality.Finally the nearest neighbor method is used to classify.Five images for everyone in the ORL database is chosen to train,and the other five images is used to test.The experimental results show that CGW has better recognition.