State Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, China.
Sensors (Basel). 2012;12(5):5551-71. doi: 10.3390/s120505551. Epub 2012 Apr 30.
When extracting discriminative features from multimodal data, current methods rarely concern themselves with the data distribution. In this paper, we present an assumption that is consistent with the viewpoint of discrimination, that is, a person's overall biometric data should be regarded as one class in the input space, and his different biometric data can form different Gaussians distributions, i.e., different subclasses. Hence, we propose a novel multimodal feature extraction and recognition approach based on subclass discriminant analysis (SDA). Specifically, one person's different bio-data are treated as different subclasses of one class, and a transformed space is calculated, where the difference among subclasses belonging to different persons is maximized, and the difference within each subclass is minimized. Then, the obtained multimodal features are used for classification. Two solutions are presented to overcome the singularity problem encountered in calculation, which are using PCA preprocessing, and employing the generalized singular value decomposition (GSVD) technique, respectively. Further, we provide nonlinear extensions of SDA based multimodal feature extraction, that is, the feature fusion based on KPCA-SDA and KSDA-GSVD. In KPCA-SDA, we first apply Kernel PCA on each single modal before performing SDA. While in KSDA-GSVD, we directly perform Kernel SDA to fuse multimodal data by applying GSVD to avoid the singular problem. For simplicity two typical types of biometric data are considered in this paper, i.e., palmprint data and face data. Compared with several representative multimodal biometrics recognition methods, experimental results show that our approaches outperform related multimodal recognition methods and KSDA-GSVD achieves the best recognition performance.
从多模态数据中提取鉴别特征时，当前的方法很少关注数据分布。在本文中，我们提出了一个与判别观点一致的假设，即一个人的整体生物特征数据应在输入空间中视为一类，而他的不同生物特征数据可以形成不同的高斯分布，即不同的子类。因此，我们提出了一种基于子类判别分析（SDA）的新的多模态特征提取和识别方法。具体来说，一个人的不同生物数据被视为一类的不同子类，并计算一个转换空间，其中属于不同人的子类之间的差异最大化，而每个子类内的差异最小化。然后，将获得的多模态特征用于分类。提出了两种解决方案来克服计算中遇到的奇异问题，分别是使用 PCA 预处理和采用广义奇异值分解（GSVD）技术。此外，我们还提供了基于 SDA 的多模态特征提取的非线性扩展，即基于 KPCA-SDA 和 KSDA-GSVD 的特征融合。在 KPCA-SDA 中，我们首先对每个单模态应用核 PCA，然后再进行 SDA。而在 KSDA-GSVD 中，我们直接对多模态数据进行核 SDA 融合，通过对 GSVD 的应用来避免奇异问题。为了简单起见，本文考虑了两种典型的生物特征数据，即掌纹数据和面部数据。与几种有代表性的多模态生物特征识别方法相比，实验结果表明，我们的方法优于相关的多模态识别方法，而 KSDA-GSVD 则实现了最佳的识别性能。
