Automatic Facial Paralysis Estimation with Facial Action Units

Ge, Xuri; Jose, Joemon M.; Wang, Pengcheng; Iyer, Arunachalam; Liu, Xiao; Huang, Han

doi:10.48550/arxiv.2203.01800

Cited by 2 publications

(4 citation statements)

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“…A few techniques based on action units have also been proposed [8]. Ge et al [8] developed the Adaptive Local-Global Relational Network (ALGRNet) for the identification of facial action units and employed it in the classification of the severity of FP. ALGRNet consists of three modules: adaptive region learning, skip-BiLSTM, feature fusion, and refining.…”

Section: Facial Action Unitsmentioning

confidence: 99%

“…Our method outperformed with an increased accuracy of 1.28%. Next, we compared the performance of our second model (i.e., FP grade classification) with the methodology used in the research [8] on FPara dataset. The FP grade classification method outperformed with an increased accuracy of 8.3%; comparison results are reported in Table 8.…”

Section: Comparing the Suggested Model To Latest Studiesmentioning

confidence: 99%

“…In related studies, various approaches like landmarks [4,6], facial action units [7,8], eye movement features [9][10][11], and convolutional neural networks (CNN) [12][13][14] are used in the feature extraction stage from the FP images and videos. This paper uses novel approaches to detect FP and its grade by combining multiple facial features (landmarks, facial action units, and eye movement features) to achieve better FP detection.…”

Section: Introductionmentioning

confidence: 99%

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Ensemble Stacking for Grading Facial Paralysis Through Statistical Analysis of Facial Features

Gogu,

Sathe

2024

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In medical diagnostics, the accurate assessment of facial paralysis (FP) represents a significant challenge, necessitating intricate analysis of facial spatial information, notably asymmetry. This condition, characterized by the inability to regulate facial muscles effectively during specific actions, often demands the discernment of clinicians, which lacks a quantitative foundation. In response to this challenge, the present study introduces two innovative models aimed at enhancing the diagnostic process for FP. The first model employs a binary classification framework to differentiate between affected individuals and those without the condition. The second, more complex model, utilizes an ensemble stacking technique to categorize the severity of FP into four distinct grades: normal, mild, moderate, and severe. Data for this analysis was sourced from a collection comprising 21 individuals diagnosed with FP and 20 healthy counterparts, extracted from publicly accessible datasets. Utilizing the OpenFace 2.0 toolkit, three categories of facial features were analyzed: landmarks, facial action units, and eye movement metrics. A comprehensive evaluation was conducted to determine the optimal model through a series of tests that integrated individual and combined facial feature sets alongside dimension reduction techniques. The findings revealed that the Support Vector Machine (SVM) method, applied to the binary classification of FP, attained an accuracy of 97.7%. Conversely, the ensemble stacking approach, incorporating Logistic Regression (LR) and SVM, demonstrated an 88.2% accuracy rate in the grading of FP severity. These outcomes suggest significant potential for the application of such models in telemedicine, facilitating early detection and ongoing remote monitoring of facial nerve functionality, thereby reducing the need for direct patientclinician encounters.

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Section: Facial Action Unitsmentioning

confidence: 99%

Section: Comparing the Suggested Model To Latest Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ensemble Stacking for Grading Facial Paralysis Through Statistical Analysis of Facial Features

Gogu,

Sathe

2024

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“…Furthermore, FACS has found applications in fields such as medicine and therapy, where it has been employed for diagnosing and treating disorders related to facial expressions, such as Möbius syndrome [7] and facial paralysis [8].…”

Section: Introductionmentioning

confidence: 99%

Using Diverse ConvNets to Classify Face Action Units in Dataset on Emotions Among Mexicans (DEM)

Moreno-Armendáriz,

Espinosa-Juarez,

Godinez-Montero

2024

IEEE Access

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To understand how Convolutional Neural Networks (ConvNets) perceive the muscular movements of the human face, known as Action Units (AUs) in this work, we introduce a new dataset named Dataset on Emotions among Mexicans (DEM), consisting of 1557 images of Mexicans labeled with twentysix AUs and seven emotions. As a benchmark, we used the comparison with DISFA+ labeled with 12 AUs. To address the task of detecting AUs in each image, six ConvNets were employed, and we evaluated their performance using the F1 Score. The two ConvNets with the best performance were VGG19 with 0.8180% (DEM), 0.9106 % (DISFA+), and ShuffleNetV2 with 0.7154% (DEM), 0.9440% (DISFA+). Subsequently, these ConvNets were analyzed using Grad-CAM and Grad-CAM++; this algorithms allows us to observe the areas of the face considered for prediction. In most cases, these areas consider the region of the labeled AU. Considering the F1 score and the visual study, we can conclude that using DEM as a dataset to classify AUs is promising since the experiments achieved performances similar to those of the current literature that only use ConvNets.INDEX TERMS Action Units, ConvNets, CAM analysis.

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Automatic Facial Paralysis Estimation with Facial Action Units

Cited by 2 publications

References 0 publications

Ensemble Stacking for Grading Facial Paralysis Through Statistical Analysis of Facial Features

Ensemble Stacking for Grading Facial Paralysis Through Statistical Analysis of Facial Features

Using Diverse ConvNets to Classify Face Action Units in Dataset on Emotions Among Mexicans (DEM)

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