Intra-Class Variation Reduction Using Training Expression Images for Sparse Representation Based Facial Expression Recognition
Intra-Class Variation Reduction Using Training Expression Images for Sparse Representation Based Facial Expression Recognition
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DOI：
10.1109/taffc.2014.2346515
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发表时间：
2014-07-01
影响因子：
11.2
通讯作者：
Ro, Yong Man
中科院分区：
文献类型：
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作者：
Lee, Seung Ho;Plataniotis, Konstantinos N. (Kostas);Ro, Yong Man
自动面部表情识别（FER）因其各种新兴应用（例如人机界面和人类情感分析）而在情感计算系统领域变得越来越重要。最近，基于稀疏表示的 FER 变得流行，并表现出了令人印象深刻的性能。然而，由于类内变化（例如身份或照明的变化），稀疏表示通常可能会为 FER 产生不太有意义的稀疏解决方案。本文提出了一种新的基于稀疏表示的 FER 方法，旨在减少类内变异，同时强调查询人脸图像中的面部表情。为此，我们提出了一种通过使用训练表情图像生成每个表情的类内变异图像的新方法。每个类内变异图像的外观在身份和照明方面可以接近查询人脸图像的外观。因此，查询人脸图像与其类内变异图像之间的差异被用作稀疏表示的表情特征。实验结果表明，所提出的 FER 方法在提高 FER 性能方面具有较高的判别能力。此外，非中性表达的类内变异图像与中性表达的类内变异图像互补，以提高FER性能。
Automatic facial expression recognition (FER) is becoming increasingly important in the area of affective computing systems because of its various emerging applications such as human-machine interface and human emotion analysis. Recently, sparse representation based FER has become popular and has shown an impressive performance. However, sparse representation could often produce less meaningful sparse solution for FER due to intra-class variation such as variation in identity or illumination. This paper proposes a new sparse representation based FER method, aiming to reduce the intra-class variation while emphasizing the facial expression in a query face image. To that end, we present a new method for generating an intra-class variation image of each expression by using training expression images. The appearance of each intra-class variation image could be close to the appearance of the query face image in identity and illumination. Therefore, the differences between the query face image and its intra-class variation images are used as the expression features for sparse representation. Experimental results show that the proposed FER method has high discriminating capability in terms of improving FER performance. Further, the intra-class variation images of non-neutral expressions are complementary with that of neutral expression, for improving FER performance.