论文标题
基于遗传特征选择的两流神经网络，用于愤怒真实性识别
A Genetic Feature Selection Based Two-stream Neural Network for Anger Veracity Recognition
论文作者
论文摘要
人们在与他人互动时可以操纵情绪表情。例如，当刺激并不真正生气时，可以表达愤怒的愤怒。在本文中，我们旨在检查观察者的瞳孔数据是否可以通过计算方法来识别愤怒的真实性。我们使用基于遗传的特征选择（GFS）方法来选择观察者的时间序列的瞳孔特征，他们观察到视频刺激的行为和真正的愤怒。然后，我们使用选定的功能来训练简单的完全连接的神经工作和两流神经网络。我们的结果表明，当两只眼睛的瞳孔响应可用时，两流体系结构能够获得有希望的识别结果，精度为93.58％。它还表明，基于遗传算法的特征选择方法可以有效地提高分类准确性3.07％。我们希望我们的工作可以帮助日常研究，例如人类机器互动和需要情感认识的心理学研究。
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 .
