Du Shan, Ward Rabab Kreidieh
Department of Electrical and Computer Engineering,The University of British Columbia, Vancouver, BC, Canada.
IEEE Trans Syst Man Cybern B Cybern. 2009 Dec;39(6):1408-19. doi: 10.1109/TSMCB.2009.2018137. Epub 2009 Apr 10.
Gabor wavelets are widely employed in face representation to decompose face images into their spatial-frequency domains. The Gabor wavelet transform, however, introduces very high dimensional data. To reduce this dimensionality, uniform sampling of Gabor features has traditionally been used. Since uniform sampling equally treats all the features, it can lead to a loss of important features while retaining trivial ones. In this paper, we propose a new face representation method that employs nonuniform multilevel selection of Gabor features. The proposed method is based on the local statistics of the Gabor features and is implemented using a coarse-to-fine hierarchical strategy. Gabor features that correspond to important face regions are automatically selected and sampled finer than other features. The nonuniformly extracted Gabor features are then classified using principal component analysis and/or linear discriminant analysis for the purpose of face recognition. To verify the effectiveness of the proposed method, experiments have been conducted on benchmark face image databases where the images vary in illumination, expression, pose, and scale. Compared with the methods that use the original gray-scale image with 4096-dimensional data and uniform sampling with 2560-dimensional data, the proposed method results in a significantly higher recognition rate, with a substantial lower dimension of around 700. The experimental results also show that the proposed method works well not only when multiple sample images are available for training but also when only one sample image is available for each person. The proposed face representation method has the advantages of low complexity, low dimensionality, and high discriminance.
伽柏小波被广泛应用于面部表征，以将面部图像分解到其空间频率域中。然而，伽柏小波变换会引入非常高维的数据。为了降低这种维度，传统上使用伽柏特征的均匀采样。由于均匀采样对所有特征一视同仁，它可能导致重要特征的丢失，同时保留了琐碎的特征。在本文中，我们提出了一种新的面部表征方法，该方法采用伽柏特征的非均匀多级选择。所提出的方法基于伽柏特征的局部统计，并使用从粗到细的分层策略来实现。对应于重要面部区域的伽柏特征会被自动选择，并比其他特征采样得更精细。然后，使用主成分分析和/或线性判别分析对非均匀提取的伽柏特征进行分类，以用于人脸识别。为了验证所提出方法的有效性，我们在基准面部图像数据库上进行了实验，其中图像在光照、表情、姿态和尺度方面存在差异。与使用具有4096维数据的原始灰度图像和具有2560维数据的均匀采样的方法相比，所提出的方法在维度显著更低（约700维）的情况下，识别率显著更高。实验结果还表明，所提出的方法不仅在有多个样本图像可用于训练时效果良好，而且在每个人只有一个样本图像时也能很好地工作。所提出的面部表征方法具有低复杂度、低维度和高判别力的优点。
