Facial expressions are a strong visual intimation of gestural behaviors. The intelligent ability to learn these non-verbal cues of the humans is the key characteristic to develop efficient human computer interaction systems. Extracting an effective representation from facial expression images is a crucial step that impacts the recognition accuracy. In this paper, we propose a novel feature selection strategy using singular value decomposition (SVD) based co-clustering to search for the most salient regions in terms of facial features that possess a high discriminating ability among all expressions. To the best of our knowledge, this is the first known attempt to explicitly perform co-clustering in the facial expression recognition domain. In our method, Gabor filters are used to extract local features from an image and then discriminant features are selected based on the class membership in co-clusters. Experiments demonstrate that co-clustering localizes the salient regions of the face image. Not only does the procedure reduce the dimensionality but also improves the recognition accuracy. Experiments on CK plus, JAFFE and MMI databases validate the existence and effectiveness of these learned facial features.

中文翻译：

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共同聚类以揭示用于表情识别的显着面部特征

面部表情是手势行为的强烈视觉暗示。学习人类这些非语言线索的智能能力是开发高效人机交互系统的关键特征。从面部表情图像中提取有效表示是影响识别精度的关键步骤。在本文中，我们提出了一种新的特征选择策略，使用基于奇异值分解 (SVD) 的共聚类来搜索最显着的区域，这些区域在所有表情中具有高区分能力。据我们所知，这是第一次在面部表情识别领域明确执行协同聚类的已知尝试。在我们的方法中，Gabor 过滤器用于从图像中提取局部特征，然后根据协同集群中的类成员选择判别特征。实验表明，共聚类可以定位人脸图像的显着区域。该过程不仅降低了维数，还提高了识别精度。在 CK plus、JAFFE 和 MMI 数据库上的实验验证了这些学习的面部特征的存在和有效性。