A Quick Review of Deep Learning in Facial Expression

Ghayoumi, Mehdi

doi:10.17265/1548-7709/2017.01.004

Cited by 18 publications

(12 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some review papers [ 19 , 20 ] have focused solely on conventional researches without introducing deep-leaning-based approaches. Recently, Ghayoumi [ 21 ] introduced a quick review of deep learning in FER. However, only a review of simple differences between conventional approaches and deep-learning-based approaches was provided.…”

Section: Introductionmentioning

confidence: 99%

A Brief Review of Facial Emotion Recognition Based on Visual Information

2018

Sensors

506

239

View full text Add to dashboard Cite

Facial emotion recognition (FER) is an important topic in the fields of computer vision and artificial intelligence owing to its significant academic and commercial potential. Although FER can be conducted using multiple sensors, this review focuses on studies that exclusively use facial images, because visual expressions are one of the main information channels in interpersonal communication. This paper provides a brief review of researches in the field of FER conducted over the past decades. First, conventional FER approaches are described along with a summary of the representative categories of FER systems and their main algorithms. Deep-learning-based FER approaches using deep networks enabling “end-to-end” learning are then presented. This review also focuses on an up-to-date hybrid deep-learning approach combining a convolutional neural network (CNN) for the spatial features of an individual frame and long short-term memory (LSTM) for temporal features of consecutive frames. In the later part of this paper, a brief review of publicly available evaluation metrics is given, and a comparison with benchmark results, which are a standard for a quantitative comparison of FER researches, is described. This review can serve as a brief guidebook to newcomers in the field of FER, providing basic knowledge and a general understanding of the latest state-of-the-art studies, as well as to experienced researchers looking for productive directions for future work.

show abstract

Section: Introductionmentioning

confidence: 99%

A Brief Review of Facial Emotion Recognition Based on Visual Information

2018

Sensors

506

239

View full text Add to dashboard Cite

show abstract

“…In recent years, deep learning (DL) approaches have emerged. DL aims to develop end-to-end systems to reduce the dependency from hand-crafted features, pre-processing, and feature extraction techniques (Ghayoumi, 2017 ). Notably, convolutional neural networks (CNNs) have been proven to be particularly efficient in this task (Mollahosseini et al, 2017 ; Zhang, 2017 ; Refat and Azlan, 2019 ).…”

Section: State Of the Artmentioning

confidence: 99%

Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives

2020

View full text Add to dashboard Cite

A fascinating challenge in the field of human–robot interaction is the possibility to endow robots with emotional intelligence in order to make the interaction more intuitive, genuine, and natural. To achieve this, a critical point is the capability of the robot to infer and interpret human emotions. Emotion recognition has been widely explored in the broader fields of human–machine interaction and affective computing. Here, we report recent advances in emotion recognition, with particular regard to the human–robot interaction context. Our aim is to review the state of the art of currently adopted emotional models, interaction modalities, and classification strategies and offer our point of view on future developments and critical issues. We focus on facial expressions, body poses and kinematics, voice, brain activity, and peripheral physiological responses, also providing a list of available datasets containing data from these modalities.

show abstract

“…ER can be mined from some information such as visual and audio data which are more important in the human emotion definition. The most data from video or image can come up from facial images which have a lot of information and facial expression can be recognized by 3 methods: a) Geometric feature-based method [43], b) Appearance-based method [23], and c) Hybrid-based method [44]. Figure 1 shows a general view for some basic and secondary human emotions which all can be extracted from video, audio, text and some other data such as silence and its duration.…”

Section: Human Emotion Analysismentioning

confidence: 99%