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首页> 外文会议>International Conference on Automatic Face and Gesture Recognition >Automatic Hidden Sadness Detection Using Micro-Expressions
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Automatic Hidden Sadness Detection Using Micro-Expressions

机译：使用微表达式自动隐藏悲伤检测

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摘要

Micro-expressions (MEs) are very short, rapid, difficult to control and subtle which reveal hidden emotions. Spotting and recognition of MEs are very difficult for humans. Lately, researchers have tried to develop automatically MEs detection and recognition algorithms, however the biggest obstacle is the lack of a suitable datasets. Previous studies mainly focus on posed rather than spontaneous videos, and the obtained performances were low. To address these challenges, firstly we made a hidden sadness database, which includes 13 video clips elicited from students, who were watching very sad scenes from the movie in the University environment. Secondly, a new approach for automatic hidden sadness detection algorithm is proposed. Finally, Support Vector Machine and Random Forest classifiers are applied, since it has been shown that they provide state-of-the-art accuracy for the facial expression recognition problem. Two experiments were conducted, one with all extracted features from the face, and the other with only eye region features. The best results are achieved with Random Forest algorithm using all face features, with the recognition rate of 95.72%. For further improvement of the performance, we plan to integrate the deep Convolutional Neural Network algorithm, due to its grow popularity in the visual recognition.
机译：微表达（MES）非常短，迅速，难以控制和微妙，揭示隐藏的情绪。对人类来说非常困难的发现和识别。最近，研究人员试图自动开发MES检测和识别算法，但最大的障碍是缺乏合适的数据集。以前的研究主要专注于构成而不是自发视频，并且获得的性能很低。为了解决这些挑战，首先我们制作了一个隐藏的悲伤数据库，其中包括从学生引起的13个视频剪辑，他们在大学环境中观看了从电影中观看了非常悲惨的场景。其次，提出了一种新的自动隐藏悲伤检测算法方法。最后，应用了支持向量机和随机林分类器，因为已经表明它们为面部表情识别问题提供了最先进的准确性。进行了两个实验，一个实验，其中来自面部的所有提取特征，另一个具有仅眼区域的特征。使用所有面部特征随机森林算法实现了最佳结果，识别率为95.72％。为了进一步提高性能，我们计划集成深度卷积神经网络算法，因为它在视觉识别中的流行度。

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