Department of Mathematics and Computer Science, University of Missouri, St. Louis, MO 63121, USA.
IEEE Trans Image Process. 2001;10(4):598-608. doi: 10.1109/83.913594.
This paper introduces a new face coding and recognition method, the enhanced Fisher classifier (EFC), which employs the enhanced Fisher linear discriminant model (EFM) on integrated shape and texture features. Shape encodes the feature geometry of a face while texture provides a normalized shape-free image. The dimensionalities of the shape and the texture spaces are first reduced using principal component analysis, constrained by the EFM for enhanced generalization. The corresponding reduced shape and texture features are then combined through a normalization procedure to form the integrated features that are processed by the EFM for face recognition. Experimental results, using 600 face images corresponding to 200 subjects of varying illumination and facial expressions, show that (1) the integrated shape and texture features carry the most discriminating information followed in order by textures, masked images, and shape images, and (2) the new coding and face recognition method, EFC, performs the best among the eigenfaces method using L(1) or L(2) distance measure, and the Mahalanobis distance classifiers using a common covariance matrix for all classes or a pooled within-class covariance matrix. In particular, EFC achieves 98.5% recognition accuracy using only 25 features.
本文提出了一种新的人脸编码和识别方法，即增强 Fisher 分类器 (EFC)，它在集成的形状和纹理特征上采用了增强的 Fisher 线性判别模型 (EFM)。形状编码人脸的特征几何形状，而纹理提供归一化的无形状图像。首先使用主成分分析 (PCA) 降低形状和纹理空间的维度，同时受 EFM 约束以增强泛化能力。然后，通过归一化过程将相应的降维形状和纹理特征组合起来，形成用于人脸识别的集成特征，这些特征由 EFM 进行处理。使用对应于 200 名不同光照和面部表情的受试者的 600 张人脸图像进行的实验结果表明：（1）集成的形状和纹理特征包含最多的鉴别信息，其次是纹理、掩蔽图像和形状图像；（2）新的编码和人脸识别方法 EFC 在使用 L(1)或 L(2)距离度量的特征脸方法、使用所有类别的公共协方差矩阵或池化的类内协方差矩阵的 Mahalanobis 距离分类器中表现最佳。特别是，EFC 仅使用 25 个特征即可实现 98.5%的识别准确率。
