深度学习
人工智能
计算机科学
领域(数学)
机器学习
特征(语言学)
情感计算
人工神经网络
光学(聚焦)
情感(语言学)
深层神经网络
心理学
语言学
哲学
物理
数学
沟通
纯数学
光学
作者
Philipp V. Rouast,Marc T. P. Adam,Raymond Chiong
出处
期刊:IEEE Transactions on Affective Computing
[Institute of Electrical and Electronics Engineers]
日期:2021-04-01
卷期号:12 (2): 524-543
被引量:127
标识
DOI:10.1109/taffc.2018.2890471
摘要
Automatic human affect recognition is a key step towards more natural human-computer interaction. Recent trends include recognition in the wild using a fusion of audiovisual and physiological sensors, a challenging setting for conventional machine learning algorithms. Since 2010, novel deep learning algorithms have been applied increasingly in this field. In this paper, we review the literature on human affect recognition between 2010 and 2017, with a special focus on approaches using deep neural networks. By classifying a total of 950 studies according to their usage of shallow or deep architectures, we are able to show a trend towards deep learning. Reviewing a subset of 233 studies that employ deep neural networks, we comprehensively quantify their applications in this field. We find that deep learning is used for learning of (i) spatial feature representations, (ii) temporal feature representations, and (iii) joint feature representations for multimodal sensor data. Exemplary state-of-the-art architectures illustrate the progress. Our findings show the role deep architectures will play in human affect recognition, and can serve as a reference point for researchers working on related applications.
科研通智能强力驱动
Strongly Powered by AbleSci AI