Visual learning of texture descriptors for facial expression recognition in thermal imagery
Visual learning of texture descriptors for facial expression recognition in thermal imagery
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DOI：
10.1016/j.cviu.2006.08.012
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发表时间：
2007-05-01
影响因子：
4.5
通讯作者：
Romero, Eva
中科院分区：
文献类型：
--
作者：
Hernandez, Benjamin;Olague, Gustavo;Romero, Eva
人脸表情识别是一个活跃的研究领域，在人类情感分析中具有潜在的应用前景。提出了一种基于长波红外图像的与光照无关的人脸表情识别方法。一般来说，人脸表情识别系统的设计考虑了可见光光谱。这使得识别过程不够健壮，无法在照明较差的环境中部署。从感兴趣区域选择、特征提取和图像分类三个方面，设计了静态图像人脸表情识别的常用方法。大多数已发表的文章都提出了以一种分离的方式解决这些任务的方法。我们提出了一种基于进化计算的视觉学习方法，它使用一个进化过程同时解决前两个任务。第一个任务在于选择一组执行特征提取的合适区域。第二个任务是调整参数，该参数定义了用于计算区域描述符的灰度共生矩阵的提取，以及描述符最佳子集的选择。这两个任务的输出被用于支持向量机委员会的分类。使用具有三个不同表达式类的热图像的数据集来验证该性能。实验结果表明，与人类观察者相比，该方法具有较好的分类效果，并具有较好的分类效果。本文的结论是：(1)热成像为FER提供了相关的信息；(2)所开发的方法可以作为不同类型模式识别问题的有效学习机制。(C)2006 Elsevier Inc.保留所有权利。
Facial expression recognition is an active research area that finds a potential application in human emotion analysis. This work presents an illumination independent approach for facial expression recognition based on long wave infrared imagery. In general, facial expression recognition systems are designed considering the visible spectrum. This makes the recognition process not robust enough to be deployed in poorly illuminated environments. Common approaches to facial expression recognition of static images are designed considering three main parts: (1) region of interest selection, (2) feature extraction, and (3) image classification. Most published articles propose methodologies that solve each of these tasks in a decoupled way. We propose a Visual Learning approach based on evolutionary computation that solves the first two tasks simultaneously using a single evolving process. The first task consists in the selection of a set of suitable regions where the feature extraction is performed. The second task consists in tuning the parameters that defines the extraction of the Gray Level Co-occurrence Matrix used to compute region descriptors, as well as the selection of the best subsets of descriptors. The output of these two tasks is used for classification by a SVM committee. A dataset of thermal images with three different expression classes is used to validate the performance. Experimental results show effective classification when compared to a human observer, as well as a PCA-SVM approach. This paper concludes that: (1) thermal Imagery provides relevant information for FER, and (2) that the developed methodology can be taken as an efficient learning mechanism for different types of pattern recognition problems. (C) 2006 Elsevier Inc. All rights reserved.