Facial Expression Recognition and Analysis: A Comparison Study of Feature Descriptors

Liew, Chun Fui; Yairi, Takehisa

doi:10.2197/ipsjtcva.7.104

Cited by 38 publications

(15 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AdaBoost [70][71][72][73] is sensitive to noisy and anomaly data. In some problems, it can be less susceptible to the overfitting problem than other learning algorithms.…”

Section: Expression Classificationmentioning

confidence: 99%

Facial Expression Recognition: A Survey

et al. 2019

View full text Add to dashboard Cite

Facial Expression Recognition (FER), as the primary processing method for non-verbal intentions, is an important and promising field of computer vision and artificial intelligence, and one of the subject areas of symmetry. This survey is a comprehensive and structured overview of recent advances in FER. We first categorise the existing FER methods into two main groups, i.e., conventional approaches and deep learning-based approaches. Methodologically, to highlight the differences and similarities, we propose a general framework of a conventional FER approach and review the possible technologies that can be employed in each component. As for deep learning-based methods, four kinds of neural network-based state-of-the-art FER approaches are presented and analysed. Besides, we introduce seventeen commonly used FER datasets and summarise four FER-related elements of datasets that may influence the choosing and processing of FER approaches. Evaluation methods and metrics are given in the later part to show how to assess FER algorithms, along with subsequent performance comparisons of different FER approaches on the benchmark datasets. At the end of the survey, we present some challenges and opportunities that need to be addressed in future.

show abstract

“…AdaBoost [70][71][72][73] is sensitive to noisy and anomaly data. In some problems, it can be less susceptible to the overfitting problem than other learning algorithms.…”

Section: Expression Classificationmentioning

confidence: 99%

Facial Expression Recognition: A Survey

et al. 2019

View full text Add to dashboard Cite

show abstract

“…So, accuracy reached up to 97% but in the case of spontaneous data (data similar to the real-life situation) like FER2013 researcher got a maximum of 75-76% accuracy. Table 2 summarize the accuracy difference between posed [15,29,31,[40][41][42][43][44] and spontaneous datasets [17,19,30,[45][46][47]. Posed datasets always get greater accuracy than spontaneous but are less reliable in real-life applications.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Facial Emotion Intensity: A Fusion Way

Pandey

Kumar

2022

SN COMPUT. SCI.

View full text Add to dashboard Cite

Many scientific works have been conducted for developing the Emotion intensity recognition system. But developing a system that is capable to estimate small to peak intensity levels with less complexity is still challenging. Therefore, we propose an effective facial emotion intensity classifier by fusion of the pre-trained deep architecture and fuzzy inference system. The pre-trained architecture VGG16 is used for basic emotion classification and it predicts emotion class with the class index value. By class index value, images are sent to the corresponding Fuzzy inference system for estimating the intensity level of detected emotion. This fusion model effectively identifies the facial emotions (happy, sad, surprise, and angry) and also predict the 13 categories of emotion intensity. This fusion model got 83% accuracy on a combined dataset (FER 2013, CK + and KDEF). The performance and findings of this proposed work are further compared with state-of-the-art models.

show abstract

“…Common facial expression classifiers include SVM algorithm (Xu et al, 2012), KNN algorithm and so on. Liew and Yairi (2015) proposed to use SVM as a classifier to classify the Hog expression features extracted in the early stage. This method has achieved good results on the JAFFE data set.…”

Section: Related Work Emotion Recognition Based On Traditional Machine Learningmentioning

confidence: 99%

MIFAD-Net: Multi-Layer Interactive Feature Fusion Network With Angular Distance Loss for Face Emotion Recognition

2021

View full text Add to dashboard Cite

Understanding human emotions and psychology is a critical step toward realizing artificial intelligence, and correct recognition of facial expressions is essential for judging emotions. However, the differences caused by changes in facial expression are very subtle, and different expression features are less distinguishable, making it difficult for computers to recognize human facial emotions accurately. Therefore, this paper proposes a novel multi-layer interactive feature fusion network model with angular distance loss. To begin, a multi-layer and multi-scale module is designed to extract global and local features of facial emotions in order to capture part of the feature relationships between different scales, thereby improving the model's ability to discriminate subtle features of facial emotions. Second, a hierarchical interactive feature fusion module is designed to address the issue of loss of useful feature information caused by layer-by-layer convolution and pooling of convolutional neural networks. In addition, the attention mechanism is also used between convolutional layers at different levels. Improve the neural network's discriminative ability by increasing the saliency of information about different features on the layers and suppressing irrelevant information. Finally, we use the angular distance loss function to improve the proposed model's inter-class feature separation and intra-class feature clustering capabilities, addressing the issues of large intra-class differences and high inter-class similarity in facial emotion recognition. We conducted comparison and ablation experiments on the FER2013 dataset. The results illustrate that the performance of the proposed MIFAD-Net is 1.02–4.53% better than the compared methods, and it has strong competitiveness.

show abstract

Facial Expression Recognition and Analysis: A Comparison Study of Feature Descriptors

Cited by 38 publications

References 51 publications

Facial Expression Recognition: A Survey

Facial Expression Recognition: A Survey

Facial Emotion Intensity: A Fusion Way

MIFAD-Net: Multi-Layer Interactive Feature Fusion Network With Angular Distance Loss for Face Emotion Recognition

Contact Info

Product

Resources

About