基于遗传特征选择的双流神经网络愤怒准确性识别
A Genetic Feature Selection Based Two-stream Neural Network for Anger Veracity Recognition
摘要：
人们在与他人互动时可以操纵情绪表达。例如，当刺激不是真正的愤怒时，可以表达行为愤怒，目的是操纵观察者。在本文中，我们的目的是检验是否可以识别愤怒的准确性与观察者的瞳孔数据与计算的方法。我们使用基于遗传的特征选择（GFS）方法来选择时间序列的瞳孔特征的观察者观察的视频刺激的行为和真正的愤怒。然后，我们使用选定的功能训练一个简单的全连接的神经网络和双流神经网络。我们的研究结果表明，双流体系结构是能够实现一个有前途的识别结果与93.58%的准确率时，从双眼的瞳孔反应是可用的。基于遗传算法的特征选择方法可以有效地提高分类精度3.07%。我们希望我们的工作能够帮助日常研究，例如需要情感识别的人机交互和心理学研究。
英文摘要：
People can manipulate emotion expressions when interacting with others. For example, acted anger can be expressed when stimuli is not genuinely angry with an aim to manipulate the observer. In this paper, we aim to examine if the veracity of anger can be recognized with observers' pupillary data with computational approaches. We use Genetic-based Feature Selection (GFS) methods to select time-series pupillary features of of observers who observe acted and genuine anger of the video stimuli. We then use the selected features to train a simple fully connected neural work and a two-stream neural network. Our results show that the two-stream architecture is able to achieve a promising recognition result with an accuracy of 93.58% when the pupillary responses from both eyes are available. It also shows that genetic algorithm based feature selection method can effectively improve the classification accuracy by 3.07%. We hope our work could help daily research such as human machine interaction and psychology studies that require emotion recognition .
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