Continuous affect prediction using eye gaze

J. O'Dwyer, R. Flynn, Niall Murray
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引用次数: 4

Abstract

In recent times, there has been significant interest in the machine recognition of human emotions, due to the suite of applications to which this knowledge can be applied. A number of different modalities, such as speech or facial expression, individually and with eye gaze, have been investigated by the affective computing research community to either classify the emotion (e.g. sad, happy, angry) or predict the continuous values of affective dimensions (e.g. valence, arousal, dominance) at each moment in time. Surprisingly after an extensive literature review, eye gaze as a unimodal input to a continuous affect prediction system has not been considered. In this context, this paper evaluates the use of eye gaze as a unimodal input to a continuous affect prediction system. The performance of continuous prediction of arousal and valence using eye gaze is compared with the performance of a speech system using the AVEC 2014 speech feature set. The experimental evaluation when using eye gaze as the single modality in a continuous affect prediction system produced a correlation result for valence prediction that is better than the correlation result obtained with the AVEC 2014 speech feature set. Furthermore, the eye gaze feature set proposed in this paper contains 98% fewer features compared to the number of features in the AVEC 2014 feature set.
使用眼睛注视进行持续影响预测
近年来,人们对人类情感的机器识别产生了浓厚的兴趣,因为这些知识可以应用到一系列的应用中。情感计算研究界已经研究了许多不同的模式,例如言语或面部表情,单独或与眼睛注视,以对情绪进行分类(例如悲伤,快乐,愤怒)或预测情感维度在每个时刻的连续值(例如效价,唤醒,支配)。令人惊讶的是,经过广泛的文献回顾,眼睛凝视作为一个单峰输入连续的影响预测系统尚未被考虑。在这种情况下,本文评估使用眼睛凝视作为单峰输入到一个连续的影响预测系统。用眼睛注视连续预测唤醒和价态的性能与使用AVEC 2014语音特征集的语音系统的性能进行了比较。在连续影响预测系统中,用眼睛注视作为单模态进行实验评价,得到的效价预测的相关结果优于AVEC 2014语音特征集的相关结果。此外,与AVEC 2014特征集相比,本文提出的眼睛凝视特征集所包含的特征数量减少了98%。
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