一种结合表情和手势的视频意见分类方法

Airton Gaio Junior, E. Santos
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引用次数: 0

摘要

大多数处理基于视频的意见识别问题的研究采用了三种不同来源的数据的组合:视频、音频和文本。因此,它们是基于复杂且依赖于语言的模型的解决方案。除了这种复杂性之外,可以观察到这些当前的解决方案在实际应用中性能较低。针对这些缺点,本文提出了一种仅以视频为数据源的意见分类方法,更准确地说,从在线视频中提取面部表情和肢体动作信息并进行组合,从而提高了分类率。该方法利用特征编码策略来改进数据表示,方便分类任务,以高精度、独立于视频语言的方式预测用户意见。利用三个公共数据库和三条基线进行了实验,对所提出的方法进行了测试。实验结果表明,即使只对视频进行视觉分析,与分析视频、音频和文本数据的基线相比,该方法的准确率和精密度提高了16%。此外,研究表明,该方法可以识别语言与训练语言不同的视频中的情绪。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Method for Opinion Classification in Video Combining Facial Expressions and Gestures
Most of the researches dealing with video-based opinion recognition problems employ the combination of data from three different sources: video, audio and text. As a consequence, they are solutions based on complex and language-dependent models. Besides such complexity, it may be observed that these current solutions attain low performance in practical applications. Focusing on overcoming these drawbacks, this work presents a method for opinion classification that uses only video as data source, more precisely, facial expression and body gesture information are extracted from online videos and combined to lead to higher classification rates. The proposed method uses feature encoding strategies to improve data representation and to facilitate the classification task in order to predict user's opinion with high accuracy and independently of the language used in videos. Experiments were carried out using three public databases and three baselines to test the proposed method. The results of these experiments show that, even performing only visual analysis of the videos, the proposed method achieves 16% higher accuracy and precision rates, when compared to baselines that analyze visual, audio and textual data video. Moreover, it is showed that the proposed method may identify emotions in videos whose language is other than the language used for training.
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