Dynamic facial expression recognition using autoregressive models

Su, Zhiming; Chen, Jingying; Chen, Haiqing

doi:10.1109/cisp.2014.7003827

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…Zhang et al 13 amalgamated appearance feature detection of facial expressions and geometric features extracted from 26 feature points, combining them with dynamic Bayesian networks for analytical purposes. Conversely, Su et al 14 proposed a facial expression recognition algorithm grounded in the fusion of geometric and appearance features, effectively leveraging the strengths of each modality to enhance the efficiency and performance of expression recognition feature extraction. By encompassing the geometric structural transformations of the face as well as the subtle structural disparities and alterations in local facial attributes, this approach captures a comprehensive representation of facial characteristics.…”

Section: Study On Facial Features Of Faces Based On Traditional Featuresmentioning

confidence: 99%

Intelligent bell facial paralysis assessment: a facial recognition model using improved SSD network

Shi,

Fan,

Zhang

et al. 2024

Sci Rep

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With the continuous progress of technology, the subject of life science plays an increasingly important role, among which the application of artificial intelligence in the medical field has attracted more and more attention. Bell facial palsy, a neurological ailment characterized by facial muscle weakness or paralysis, exerts a profound impact on patients’ facial expressions and masticatory abilities, thereby inflicting considerable distress upon their overall quality of life and mental well-being. In this study, we designed a facial attribute recognition model specifically for individuals with Bell’s facial palsy. The model utilizes an enhanced SSD network and scientific computing to perform a graded assessment of the patients’ condition. By replacing the VGG network with a more efficient backbone, we improved the model’s accuracy and significantly reduced its computational burden. The results show that the improved SSD network has an average precision of 87.9% in the classification of light, middle and severe facial palsy, and effectively performs the classification of patients with facial palsy, where scientific calculations also increase the precision of the classification. This is also one of the most significant contributions of this article, which provides intelligent means and objective data for future research on intelligent diagnosis and treatment as well as progressive rehabilitation.

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Section: Study On Facial Features Of Faces Based On Traditional Featuresmentioning

confidence: 99%

Intelligent bell facial paralysis assessment: a facial recognition model using improved SSD network

Shi,

Fan,

Zhang

et al. 2024

Sci Rep

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“…The specialty of Gabor Wavelets [11][12][13][14] is that they extract local features of an image even at different orientations in spatial as well as frequency domains. The GWs finds essential features in an image such as frequency selectivity, orientation selectivity, spatial localization, and quadrature phase relationship.…”

Section: Gabor Waveletsmentioning

confidence: 99%

A Facial Expression Recognition Model using Support Vector Machines

Bellamkonda¹,

Gopalan²

2018

IJMSC

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Facial Expression Recognition (FER) has gained interest among researchers due to its inevitable role in the human computer interaction. In this paper, an FER model is proposed using principal component analysis (PCA) as the dimensionality reduction technique, Gabor wavelets and Local binary pattern (LBP) as the feature extraction techniques and support vector machine (SVM) as the classification technique. The experimentation was done on Cohn-Kanade, JAFFE, MMI Facial Expression datasets and real time facial expressions using a webcam. The proposed methods outperform the existing methods surveyed.

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“…This kind of ability needs to be given to the robots to make the human-robot interaction to be much humanistic than mechanical. There are a good number of feature extraction, and classification studies are available in literature [3][4][5][6][7][8][9][10][11][12] and SVM appears to be a popular classifier for FER systems although Neural Networks [13][14][15][16], Hidden Markov Models [17,18] and KNNs [19,20] have also extensively used in similar such studies.…”

Section: Introductionmentioning

confidence: 99%

Pattern Averaging Technique for Facial Expression Recognition Using Support Vector Machines

Gopalan¹,

Bellamkonda²

2018

IJIGSP

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Facial expression is one of the nonverbal communication methods of identifying an emotional state of a human being. Due to its crucial importance in Human-Robot interaction, facial expression recognition (FER) is in the limelight of recent research activities. Most of the studies consider the whole expression images in their analysis, and it has several has several drawbacks concerning illumination, orientation, texture, zoom level, time and space complexity. In this paper, a novel feature extraction technique called the pattern averaging is studied on whole image data using reduction in the dimension of the image by averaging the neighboring pixels. The study is found to give better results on standard datasets using support vector machine classifier.

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Dynamic facial expression recognition using autoregressive models

Cited by 4 publications

References 22 publications

Intelligent bell facial paralysis assessment: a facial recognition model using improved SSD network

Intelligent bell facial paralysis assessment: a facial recognition model using improved SSD network

A Facial Expression Recognition Model using Support Vector Machines

Pattern Averaging Technique for Facial Expression Recognition Using Support Vector Machines

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