过度拟合
深度学习
人工智能
面部表情
计算机科学
特征提取
判别式
任务(项目管理)
面部识别系统
预处理器
领域(数学)
卷积神经网络
机器学习
模式识别(心理学)
特征(语言学)
人工神经网络
工程类
数学
哲学
语言学
系统工程
纯数学
作者
Mohan Karnati,Ayan Seal,Debotosh Bhattacharjee,Anis Yazidi,Ondřej Krejcar
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:72: 1-31
被引量:23
标识
DOI:10.1109/tim.2023.3243661
摘要
Emotion recognition plays a significant role in cognitive psychology research. However, measuring emotions is a challenging task. Thus, several approaches have been designed for facial expression recognition (FER). Although, the challenges increase further as the data transit from the laboratory-controlled environment to in-the-wild circumstances, nowadays, applications are overwhelmed by a profusion of deep learning (DL) techniques in real-world problems. DL networks have steadily led to a better understanding of low-dimensional discriminative features from high-dimensional complex face patterns for automatic FER. The modern FER systems based on deep neural networks mainly suffer from two problems: overfitting due to the inadequate availability of training data and complications unassociated with the expressions, such as occlusion, posture, illumination, and identity bias. This study aims to provide a comprehensive survey of the significant DL-based methods that have made a notable contribution to the field of FER. Different components of the methods, such as preprocessing, feature extraction, and classification of facial expressions, are described systematically. Moreover, the discussed approaches are analyzed to compare their performance along with their advantages and limitations. Furthermore, different databases relevant to FER are also explored in this study. Essentially, the main aim of this survey is twofold. The former is to discuss the current scenario of FER approaches and the latter is to present some thoughts on the future directions of facial emotion recognition by machines: what are the obstacles and prospects for FER researchers?
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