Center for Biometrics and Security Research and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
IEEE Trans Image Process. 2011 Jan;20(1):247-56. doi: 10.1109/TIP.2010.2060207. Epub 2010 Jul 19.
Information jointly contained in image space, scale and orientation domains can provide rich important clues not seen in either individual of these domains. The position, spatial frequency and orientation selectivity properties are believed to have an important role in visual perception. This paper proposes a novel face representation and recognition approach by exploring information jointly in image space, scale and orientation domains. Specifically, the face image is first decomposed into different scale and orientation responses by convolving multiscale and multiorientation Gabor filters. Second, local binary pattern analysis is used to describe the neighboring relationship not only in image space, but also in different scale and orientation responses. This way, information from different domains is explored to give a good face representation for recognition. Discriminant classification is then performed based upon weighted histogram intersection or conditional mutual information with linear discriminant analysis techniques. Extensive experimental results on FERET, AR, and FRGC ver 2.0 databases show the significant advantages of the proposed method over the existing ones.
图像空间、尺度和方向域中联合包含的信息可以提供在这些域中任何一个都无法看到的丰富重要线索。位置、空间频率和方向选择性特性被认为在视觉感知中具有重要作用。本文通过探索图像空间、尺度和方向域中的联合信息，提出了一种新的人脸表示和识别方法。具体来说，首先通过卷积多尺度和多方向 Gabor 滤波器将人脸图像分解为不同的尺度和方向响应。其次，局部二值模式分析用于描述不仅在图像空间中，而且在不同尺度和方向响应中的邻域关系。这样，就可以从不同的域中探索信息，从而为识别提供良好的人脸表示。然后基于加权直方图交或条件互信息与线性判别分析技术进行判别分类。在 FERET、AR 和 FRGC ver 2.0 数据库上的广泛实验结果表明，与现有方法相比，该方法具有显著的优势。
