Facial expression recognition from near-infrared videos
Facial expression recognition from near-infrared videos
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DOI：
10.1016/j.imavis.2011.07.002
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发表时间：
2011-08
期刊：
影响因子：
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通讯作者：
Guoying Zhao;Xiaohua Huang;M. Taini;S. Li;M. Pietikäinen
中科院分区：
文献类型：
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作者：
Guoying Zhao;Xiaohua Huang;M. Taini;S. Li;M. Pietikäinen
人脸表情识别就是要确定人脸的情绪状态而不考虑其身份。大多数现有的面部表情数据集都是在可见光谱中捕获的。然而，可见光（维斯）会随着时间和地点而变化，从而导致外观和纹理的显著变化。在本文中，我们提出了一种新的研究动态面部表情识别，使用近红外（NIR）视频序列和LBP-TOP（本地二进制模式从三个正交平面）的特征描述符。近红外成像结合LBP-TOP特征提供了人脸视频序列的光照不变描述。切片中的外观和运动特征用于表情分类，为此，从训练示例中学习区分权重。此外，提出了基于组件的面部特征，将联合收割机的几何和外观信息结合起来，为表情的表示提供了一种有效的方法。实验结果表明，基于Oulu-CASIA NIR&维斯人脸表情数据库、支持向量机和稀疏表示分类器的人脸表情识别对光照变化具有良好的鲁棒性。这为未来基于NIR的面部表情识别研究提供了一个基线。
Facial expression recognition is to determine the emotional state of the face regardless of its identity. Most of the existing datasets for facial expressions are captured in a visible light spectrum. However, the visible light (VIS) can change with time and location, causing significant variations in appearance and texture. In this paper, we present a novel research on a dynamic facial expression recognition, using near-infrared (NIR) video sequences and LBP-TOP (Local binary patterns from three orthogonal planes) feature descriptors. NIR imaging combined with LBP-TOP features provide an illumination invariant description of face video sequences. Appearance and motion features in slices are used for expression classification, and for this, discriminative weights are learned from training examples. Furthermore, component-based facial features are presented to combine geometric and appearance information, providing an effective way for representing the facial expressions. Experimental results of facial expression recognition using a novel Oulu-CASIA NIR&VIS facial expression database, a support vector machine and sparse representation classifiers show good and robust results against illumination variations. This provides a baseline for future research on NIR-based facial expression recognition.