Automatic recognition of facial movement for paralyzed face

Wang, Ting; Dong, Junyu; Sun, Xin; Shu, Zhang; Wang, Shengke

doi:10.3233/bme-141093

Cited by 25 publications

(27 citation statements)

References 29 publications

(32 reference statements)

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“…Even with 1 disagreement, which allows for more experimental errors, the result is significantly worse than ours. Wang [29,30] used SVM with RBF to measure accuracy. The result showed our method is better than their method in MV2-6.…”

Section: Discussionmentioning

confidence: 99%

“…The result is shown in Table 2 (Columns 5-6, RBF with 0/1 disagreement). Wang et al [29,30] presented a novel method for grading facial paralysis integrating both static facial asymmetry and dynamic transformation factors. Wang used an SVM with the RBF kernel function to quantify the static facial asymmetry on images using five of the six facial movements (MV1-6), but they did not measure accuracy of MV0.…”

Section: Comparison With Other Computer-aided Analysis Systemsmentioning

confidence: 99%

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Neurologist Standard Classification of Facial Nerve Paralysis with Deep Neural Networks

Song

Ding

et al. 2018

Future Internet

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Facial nerve paralysis (FNP) is the most common form of facial nerve damage, which leads to significant physical pain and abnormal function in patients. Traditional FNP detection methods are based on visual diagnosis, which relies solely on the physician’s assessment. The use of objective measurements can reduce the frequency of errors which are caused by subjective methods. Hence, a fast, accurate, and objective computer method for FNP classification is proposed that uses a single Convolutional neural network (CNN), trained end-to-end directly from images, with only pixels and disease labels as inputs. We trained the CNN using a dataset of 1049 clinical images and divided the dataset into 7 categories based on classification standards with the help of neurologists. We tested its performance against the neurologists’ ground truth, and our results matched the neurologists’ level with 97.5% accuracy.

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Section: Discussionmentioning

confidence: 99%

Section: Comparison With Other Computer-aided Analysis Systemsmentioning

confidence: 99%

Neurologist Standard Classification of Facial Nerve Paralysis with Deep Neural Networks

Song

Ding

et al. 2018

Future Internet

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“…Back propagation is based on the loss function used in the training stage. Keeping in view the underlying classification problem, the categorical cross entropy has been used as the loss function, as shown in Equation (2).…”

Section: Classificationmentioning

confidence: 99%

“…The hand-crafted methods rely on prior knowledge to extract the underlying asymmetrical features. The representative works in this category include [1][2][3][4]. Kim et al [1] proposed an automatic diagnosis system for facial nerve palsy.…”

Section: Introductionmentioning

confidence: 99%

“…But this study is suggested for smart-phone based smaller applications and thus not suitable for large applications such as diagnosis systems required for primary health care [5]. Wang et al [2] used an active shape model and local binary patterns to encode actions for palsy-affected faces. He et al [3] demonstrated a facial palsy grading system employing multiresolution local binary patterns along with SVM as classifier.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Grading of Palsy Using Asymmetrical Facial Features: A Study Complemented by New Solutions

et al. 2018

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Facial palsy caused by nerve damage results in loss of facial symmetry and expression. A reliable palsy grading system for large-scale applications is still missing in the literature. Although numerous approaches have been reported on facial palsy quantification and grading, most employ hand-crafted features on relatively smaller datasets which limit the classification accuracy due to non-optimal face representation. In contrast, convolutional neural networks (CNNs) automatically learn the discriminative features facilitating the accurate classification of underlying tasks. In this paper, we propose to apply a typical deep network on a large dataset to extract palsy-specific features from face images. To prevent the inherent limitation of overfitting frequently occurring in CNNs, a generative adversial network (GAN) is applied to augment the training dataset. The deeply learned features are then used to classify the palsy disease into five benchmarked grades. The experimental results show that the proposed approach offers superior palsy grading performance compared to some existing methods. Such an approach is useful for palsy grading at large scale, such as primary health care.

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