Solving the small sample size problem in face recognition using generalized discriminant analysis
Solving the small sample size problem in face recognition using generalized discriminant analysis
复制标题
DOI：
10.1016/j.patcog.2005.06.013
复制
发表时间：
2006-02
期刊：
影响因子：
--
通讯作者：
P. Howland;Jianlin Wang;Haesun Park
中科院分区：
文献类型：
--
作者：
P. Howland;Jianlin Wang;Haesun Park
人脸识别的目标是通过面部图像来区分人。每个人的图像形成一个簇，通过将新图像分配到正确的簇来识别新图像。由于图像的维数非常高，因此有必要降低其维数。线性判别分析 (LDA) 已被证明可以有效降低维度，同时保留数据的簇结构。它通常被定义为涉及协方差矩阵的优化问题，协方差矩阵代表簇内和簇间的分散。这些矩阵之一必须是非奇异的这一要求将其应用限制在数据维度不超过样本大小的数据集中。然而，对于人脸识别，尺寸通常超过数据库中图像的数量，导致所谓的小样本问题。最近，通过使用广义奇异值分解（GSVD）来规避非奇异性要求，LDA的适用性得到了扩展，从而使LDA可以直接应用于人脸识别数据。我们的实验证实，与其他现有方法相比，LDA/GSVD 非常有效地解决了小样本量问题。
The goal of face recognition is to distinguish persons via their facial images. Each person's images form a cluster, and a new image is recognized by assigning it to the correct cluster. Since the images are very high-dimensional, it is necessary to reduce their dimension. Linear discriminant analysis (LDA) has been shown to be effective at dimension reduction while preserving the cluster structure of the data. It is classically defined as an optimization problem involving covariance matrices that represent the scatter within and between clusters. The requirement that one of these matrices be nonsingular restricts its application to datasets in which the dimension of the data does not exceed the sample size. For face recognition, however, the dimension typically exceeds the number of images in the database, resulting in what is referred to as the small sample size problem. Recently, the applicability of LDA has been extended by using the generalized singular value decomposition (GSVD) to circumvent the nonsingularity requirement, thus making LDA directly applicable to face recognition data. Our experiments confirm that LDA/GSVD solves the small sample size problem very effectively as compared with other current methods.