基于模糊积分的不完全小波包子空间集成人脸识别

摘要

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.%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。