CAJ | 학술논문

提出一种基于模糊积分的不完全小波包子空间集成人脸识别方法，并与五种相关方法进行实验比较。首先对人脸图像做不完全小波包分解，对双向低频子空间图像直接进行特征提取，对含有一个方向低频成分的高频子空间图像先求平均，再进行提取特征；然后用得到的不同子空间图像训练模糊分类器；最后用模糊积分融合训练的模糊分类器。该方法能够充分利用不同频率小波子空间图像中包含的有用信息，从而提高人脸识别的精度。在ORL、YALE、JAFFE和FERET这4个人脸数据库上进行实验，实验结果表明该方法在识别精度方面均优于五种相关方法。
제출일충기우모호적분적불완전소파포자공간집성인검식별방법，병여오충상관방법진행실험비교。수선대인검도상주불완전소파포분해，대쌍향저빈자공간도상직접진행특정제취，대함유일개방향저빈성분적고빈자공간도상선구평균，재진행제취특정；연후용득도적불동자공간도상훈련모호분류기；최후용모호적분융합훈련적모호분류기。해방법능구충분이용불동빈솔소파자공간도상중포함적유용신식，종이제고인검식별적정도。재ORL、YALE、JAFFE화FERET저4개인검수거고상진행실험，실험결과표명해방법재식별정도방면균우우오충상관방법。
An ensemble incomplete wavelet packet subspaces method based on fuzzy integral for face recognition is proposed, and it is compared with 5 related approaches. Firstly, face images are decomposed into different sub-images with incomplete wavelet packet transform. For sub-images with low frequency information in two directions, features are extracted directly. And for high frequency sub-images with low frequency information in one direction, features are extracted after these images are averaged. Next, fuzzy classifiers are trained by the obtained wavelet subspace images. Finally, the trained classifiers are integrated by fuzzy integral. The proposed method makes full use of the information provided by sub-images with different frequency and improves the accuracy of face recognition. The experimental results on ORL, YALE, JAFFE and FERET show that the proposed method has higher accuracy than 5 related approaches.