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首页> 外文期刊>Multimedia Tools and Applications >Recognition of facial expressions based on salient geometric features and support vector machines
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Recognition of facial expressions based on salient geometric features and support vector machines

机译：基于显着几何特征和支持向量机的面部表情识别

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摘要

Facial expressions convey nonverbal cues which play an important role in interpersonal relations, and are widely used in behavior interpretation of emotions, cognitive science, and social interactions. In this paper we analyze different ways of representing geometric feature and present a fully automatic facial expression recognition (FER) system using salient geometric features. In geometric feature-based FER approach, the first important step is to initialize and track dense set of facial points as the expression evolves over time in consecutive frames. In the proposed system, facial points are initialized using elastic bunch graph matching (EBGM) algorithm and tracking is performed using Kanade-Lucas-Tomaci (KLT) tracker. We extract geometric features from point, line and triangle composed of tracking results of facial points. The most discriminative line and triangle features are extracted using feature selective multi-class AdaBoost with the help of extreme learning machine (ELM) classification. Finally the geometric features for FER are extracted from the boosted line, and triangles composed of facial points. The recognition accuracy using features from point, line and triangle are analyzed independently. The performance of the proposed FER system is evaluated on three different data sets: namely CK+, MMI and MUG facial expression data sets.
机译：面部表情传达了非语言线索，这些线索在人际关系中起着重要作用，并广泛用于情感的行为解释，认知科学和社会互动中。在本文中，我们分析了表示几何特征的不同方法，并提出了一种使用显着几何特征的全自动面部表情识别（FER）系统。在基于几何特征的FER方法中，第一步很重要，就是随着表情在连续帧中随时间的变化而初始化并跟踪密集的面部点集。在提出的系统中，使用弹性束图匹配（EBGM）算法初始化面部点，并使用Kanade-Lucas-Tomaci（KLT）跟踪器执行跟踪。我们从点，线和三角形提取的几何特征构成了面部点的跟踪结果。在极端学习机（ELM）分类的帮助下，使用特征选择多类AdaBoost提取最具区别性的线和三角形特征。最后，从增强线和由面部点组成的三角形中提取FER的几何特征。独立分析使用点，线和三角形的特征进行的识别精度。在三个不同的数据集上评估提出的FER系统的性能：即CK +，MMI和MUG面部表情数据集。

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