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。
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.
出处
《光电工程》
CAS
CSCD
北大核心
2010年第11期140-144,150,共6页
Opto-Electronic Engineering
基金
陕西省教育厅科学研究项目:多尺度流形学习降维理论体系研究
关键词
GABOR小波
流形学习
核函数
核局部线性嵌入
人脸识别
Gabor wavelet
manifold learning
kernel function
kernel local linear embedding
face recognition
