光电工程, 2010, 37 (11): 140, 网络出版: 2011-01-05  

Curvelet域流形学习人脸识别算法研究

Curvelet-based Manifold Learning for Face Recognition
作者单位
西安理工大学 自动化与信息工程学院,西安 710048
摘要
Curvelet 是一种多尺度多方向的图像变换工具,能有效克服小波在表达图像沿边缘奇异特征时的冗余,形成特征的稀疏表达。进一步考虑高维图像可能存在于一个低维流形上,所以提出将曲波提取到的特征应用流形学习处理以发现其低维结构应用于人脸识别。实验表明Curvelet 提取到的特征经LLE 处理后能找到优于LLE 下的流形结构。和已有Gabor 结合流形学习人脸识别的比较研究说明,曲波结合流形学习的方法获得了高于Gabor 结合流形学习的识别率,在Essex 表情库和YaleB 光照库上的实验证明了这一点。
Abstract
Curvelet is a multiscale and multidirectional image transformation tool, which can efficiently overcome the redundancy of wavelet in expressing the singular feature along curves of the image, and can obtain a sparse feature representation. Moreover, based on the consideration that high-dimensional image may exist in lower dimensional manifolds, manifold learning is performed on the Curvelet features so as to find low-dimensional structures, which is used for face recognition. Experiments show that the Curvelet features further processed by LLE show better clustering ability than the LLE. Compared with the already existing Gabor-based manifold learning, Curvelet-based manifold learning perform better under both facial expression and illumination changes, and either case sees valuable improvements. Experiments in the Essex expression and Yale B lighting face databases prove this point.

张九龙, 张志禹, 焦妍, 夏春丽. Curvelet域流形学习人脸识别算法研究[J]. 光电工程, 2010, 37(11): 140. ZHANG Jiu-long, ZHANG Zhi-yu, JIAO Yan, XIA Chun-li. Curvelet-based Manifold Learning for Face Recognition[J]. Opto-Electronic Engineering, 2010, 37(11): 140.

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