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首页> 外文期刊>Journal of visual communication & image representation >Hierarchical uncorrelated multiview discriminant locality preserving projection for multiview facial expression recognition
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Hierarchical uncorrelated multiview discriminant locality preserving projection for multiview facial expression recognition

机译：用于多视图面部表情识别的分层不相关多视图判别局部性保留投影

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摘要

Existing multi-view facial expression recognition algorithms are not fully capable of finding discriminative directions if the data exhibits multi-modal characteristics. This research moves toward addressing this issue in the context of multi-view facial expression recognition. For multi-modal data, local preserving projection (LPP) or local Fisher discriminant analysis (LFDA)-based approach is quite appropriate to find a discriminative space. Also, the classification performance can be enhanced by imposing uncorrelated constraint onto the discriminative space. So for multi-view (multi-modal) data, we proposed an uncorrelated multi-view discriminant locality preserving projection (UMvDLPP)-based approach to find an uncorrelated common discriminative space. Additionally, the proposed UMvDLPP is implemented in a hierarchical fashion (H-UMvDLPP) to obtain an optimal performance. Extensive experiments on BU3DFE dataset show that UMvDLPP performs slightly better than the existing methods. However, an improvement of approximately 3% as compared to the existing state-of-the-art multi-view learning-based approaches is achieved by our H-UMvDLPP. This improvement is due to the fact that the proposed method enhances the discrimination between the classes more effectively, and classifies expressions category-wise followed by classification of the basic expressions embedded in each of the subcategories (hierarchical approach).
机译：如果数据表现出多模式特征，则现有的多视图面部表情识别算法不能完全找到判别方向。这项研究致力于在多视图面部表情识别的背景下解决这个问题。对于多模式数据，基于局部保留投影（LPP）或基于局部Fisher判别分析（LFDA）的方法非常适合查找判别空间。同样，可以通过将不相关的约束强加到区分空间上来提高分类性能。因此，对于多视图（多模式）数据，我们提出了一种基于不相关多视图判别局部性保留投影（UMvDLPP）的方法，以找到不相关的公共判别空间。此外，建议的UMvDLPP以分层方式（H-UMvDLPP）实施以获得最佳性能。在BU3DFE数据集上进行的大量实验表明，UMvDLPP的性能比现有方法稍好。但是，与我们现有的基于H-UMvDLPP的最新的基于多视图学习的最新方法相比，大约可提高3％。该改进是由于以下事实：所提出的方法可以更有效地增强类别之间的区分，并按类别对表达式进行分类，然后对嵌入在每个子类别中的基本表达式进行分类（分层方法）。

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