A method of facial expression recognition based on Gabor and NMF

Zhou, Jun; Zhang, Sue; Mei, Hongyan; Wang, Dawei

doi:10.1134/s1054661815040070

Cited by 21 publications

(4 citation statements)

References 3 publications

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“…9 Feature extraction methods are mainly divided into two categories, one is the extraction method based on dynamic features. It uses the geometric feature extraction of key points of the face in the image sequence, 10 texture feature extraction, 11 optical flow method, 12 and differential image method for facial expression recognition. Texture is an important feature to express an image, it does not depend on color or brightness and reflects the homogeneity of the image, and reflects important information about the organization and arrangement of the surface structure and their connection with the surrounding environment.…”

Section: Related Workmentioning

confidence: 99%

(Retracted) Facial expression recognition in classroom environment based on improved Xception model

Tian

Fang

2022

J. Electron. Imag.

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Nowadays, there is a large amount of research on facial expression, and researchers have put forward a variety of effective methods. Now, due to the unsupervised learning function, deep learning is increasingly applied to facial expression recognition. The purpose of this paper is to study the recognition of facial expressions in a classroom environment based on an improved anomaly model. This paper proposes a new face recognition model, which is based on convolutional neural network. This paper introduces the construction and algorithm of the model and tests its performance for expression recognition through experiments. The experimental results show that the expression recognition accuracy of the facial expression recognition model proposed is 72.4%. The experiment compares this model with other models, the recognition accuracy of the proposed model architecture is 72.4%, and finds that this model has the best classification accuracy and the least parameters.

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Section: Related Workmentioning

confidence: 99%

(Retracted) Facial expression recognition in classroom environment based on improved Xception model

Tian

Fang

2022

J. Electron. Imag.

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“…People use different types of facial expressions to demonstrate different types of social intentions. In [7], it is mentioned that facial expressions can contribute up to 55% of the information exchanged during an interaction. Due to the high ratio, facial expressions holds an important place in personal relationships and emotion-based communication [8].…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Recognition and beyond for Compound Facial Emotion Recognition

et al. 2021

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Facial emotion recognition is an inherently complex problem due to individual diversity in facial features and racial and cultural differences. Moreover, facial expressions typically reflect the mixture of people’s emotional statuses, which can be expressed using compound emotions. Compound facial emotion recognition makes the problem even more difficult because the discrimination between dominant and complementary emotions is usually weak. We have created a database that includes 31,250 facial images with different emotions of 115 subjects whose gender distribution is almost uniform to address compound emotion recognition. In addition, we have organized a competition based on the proposed dataset, held at FG workshop 2020. This paper analyzes the winner’s approach—a two-stage recognition method (1st stage, coarse recognition; 2nd stage, fine recognition), which enhances the classification of symmetrical emotion labels.

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“…It is determined by a vector sequence formed between key feature points established by some statistical shape models, which can well describe the changes in size, shape, and position caused by changes of facial expressions. In the past few years, many works [8][9][10][11][12][13][14] focus on using Gabor, LBP, and geometric features for FER. Gabor and LBP features have a strong description of local texture and more detailed expression features, but they are not robust.…”

Section: Introductionmentioning

confidence: 99%

Facial Expression Recognition Algorithm Based on Fusion of Transformed Multilevel Features and Improved Weighted Voting SVM

Meng

Yuan

et al. 2021

Mathematical Problems in Engineering

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In allusion to the shortcomings of traditional facial expression recognition (FER) that only uses a single feature and the recognition rate is not high, a FER method based on fusion of transformed multilevel features and improved weighted voting SVM (FTMS) is proposed. The algorithm combines the transformed traditional shallow features and convolutional neural network (CNN) deep semantic features and uses an improved weighted voting method to make a comprehensive decision on the results of the four trained SVM classifiers to obtain the final recognition result. The shallow features include local Gabor features, LBP features, and joint geometric features designed in this study, which are composed of distance and deformation characteristics. The deep feature of CNN is the multilayer feature fusion of CNN proposed in this study. This study also proposes to use a better performance SVM classifier with CNN to replace Softmax since the poor distinction between facial expressions. Experiments on the FERPlus database show that the recognition rate of this method is 17.2% higher than that of the traditional CNN, which proves the effectiveness of the fusion of the multilayer convolutional layer features and SVM. FTMS-based facial expression recognition experiments are carried out on the JAFFE and CK+ datasets. Experimental results show that, compared with the single feature, the proposed algorithm has higher recognition rate and robustness and makes full use of the advantages and characteristics of different features.

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A method of facial expression recognition based on Gabor and NMF

Cited by 21 publications

References 3 publications

(Retracted) Facial expression recognition in classroom environment based on improved Xception model

(Retracted) Facial expression recognition in classroom environment based on improved Xception model

Two-Stage Recognition and beyond for Compound Facial Emotion Recognition

Facial Expression Recognition Algorithm Based on Fusion of Transformed Multilevel Features and Improved Weighted Voting SVM

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