Maximum neighborhood margin criterion in face recognition
Maximum neighborhood margin criterion in face recognition
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DOI：
10.1117/1.3122033
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发表时间：
2009-04
影响因子：
1.3
通讯作者：
Pang Ying Han;A. Teoh
中科院分区：
文献类型：
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作者：
Pang Ying Han;A. Teoh
特征提取是一种数据分析技术，致力于消除冗余并提取最具辨别力的信息。在人脸识别中，特征提取器通常受到小样本量问题的困扰，其中训练图像的总数远小于图像维度。最近，提出了一种优化的面部特征提取器，最大边缘标准（MMC）。 MMC通过以标准形式求解广义特征值问题来计算优化投影，无需逆矩阵运算，因此不会受到样本量小问题的影响。然而，MMC 本质上是一种线性投影技术，它依赖于面部图像像素强度来计算类内和类间散射。人脸的非线性特性限制了 MMC 的辨别能力。因此，我们提出了一种改进的MMC，即最大邻域边缘准则（MNMC）。与 MMC 不同，MMC 保留了不能完美描述底层面流形的全局几何结构，MNMC 寻求一种通过邻域保留来保留局部几何结构的投影。该目标函数导致分类能力的增强，这已被实验结果所证明。与 MMC 相比，MNMC 显示出其性能优势，尤其是在姿态、光照和表情（PIE）和人脸识别大挑战（FRGC）数据库中。
Feature extraction is a data analysis technique devoted to removing redundancy and extracting the most discriminative information. In face recognition, feature extractors are normally plagued with small sample size problems, in which the total number of training images is much smaller than the image dimensionality. Recently, an optimized facial feature extractor, maximum marginal criterion (MMC), was proposed. MMC computes an optimized projection by solving the generalized eigenvalue problem in a standard form that is free from inverse matrix operation, and thus it does not suffer from the small sample size problem. However, MMC is essentially a linear projection technique that relies on facial image pixel intensity to compute within- and between-class scatters. The nonlinear nature of faces restricts the discrimination of MMC. Hence, we propose an improved MMC, namely maximum neighborhood margin criterion (MNMC). Unlike MMC, which preserves global geometric structures that do not perfectly describe the underlying face manifold, MNMC seeks a projection that preserves local geometric structures via neighborhood preservation. This objective function leads to the enhancement of classification capability, and this is testified by experimental results. MNMC shows its performance superiority compared to MMC, especially in pose, illumination, and expression (PIE) and face recognition grand challenge (FRGC) databases.