Quantitative analysis of facial paralysis based on limited-orientation modified circular Gabor filters

Ngo, Truc Hung; Seo, Masataka; Matsushiro, Naoki; Xiong, Wei; Chen, Yen‐Wei

doi:10.1109/icpr.2016.7899658

Cited by 13 publications

(14 citation statements)

References 9 publications

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“…To address this problem, measures upon image pixel intensities could be adopted, e.g. distances between pixel intensities [41]- [43] or mediate visual texture descriptors such as Local Binary Pattern (LBP) histogram features [30] and circular Gabor features [44] from two sides of the face. wrinkle, eye closure, nose wrinkle, smile and lip pucker [7], [39], [45] (see Fig.…”

Section: A Computational Measures In 2dmentioning

confidence: 99%

“…This contrast manifests clear evidence of asymmetry, thus should not be excluded. To this end, analogous measures [30], [38], [44], [48]- [50] driven by pixel intensities as those used in static measures could be applied. A simple solution is to perform a subtraction between images obtained at rest and during facial movement, then compare the luminance changes of a specific paretic area with that of the healthy side [48], [49].…”

Section: A Computational Measures In 2dmentioning

confidence: 99%

“…Such kind of methods however is sensitive to illumination changes, which is restricted to environment with controlled lighting. To cross this constraint, some studies resorted to robust visual texture descriptors [30], [38], [44], [50]. He et al [30] employed the multi-resolution LBP (MLBP) on temporal-spatial domain to extract the motion features from each region of the face.…”

Section: A Computational Measures In 2dmentioning

confidence: 99%

“…Then they assessed the symmetry of facial motion by the Resistor-Average Distance (RAD, a distance measure between two probability distributions that is closely related to the Kullback-Leibler distance) between MLBP features. NGO et al further extended the facial texture analysis from spatial domain to frequency domain by using Gabor filters [50], circular Gabor filters [44]. More recent studies [14], [51] turned to deep learning methods such as convolutional neural networks (CNNs) which have revolutionized the visual imagery analysis to extract high-level features from the face image.…”

Section: A Computational Measures In 2dmentioning

confidence: 99%

“…For a new subject, the solution first extracts computational features from his/her facial movement data, then calls a pre-trained classifier to map the features to the facial nerve function grade defined in the grading scale. The most frequently used grading scale is House-Brackmann scale (HBS) which divides the facial nerve function into six levels [11], [12], [30], [51], followed by Yanagihara scale (YGS) [44], [50] and Sunnybrook scale (SGS, see Table I) [47]. The grade could also simply be a binary value indicating whether the subject has facial palsy or not [37], [46], or if a specific face region is paralyzed [14].…”

Section: Assessment Outcomesmentioning

confidence: 99%

See 4 more Smart Citations

A Review on Automated Facial Nerve Function Assessment From Visual Face Capture

Lou

Wang

2020

IEEE Trans. Neural Syst. Rehabil. Eng.

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Section: A Computational Measures In 2dmentioning

confidence: 99%

Section: A Computational Measures In 2dmentioning

confidence: 99%

Section: A Computational Measures In 2dmentioning

confidence: 99%

Section: A Computational Measures In 2dmentioning

confidence: 99%

Section: Assessment Outcomesmentioning

confidence: 99%

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A Review on Automated Facial Nerve Function Assessment From Visual Face Capture

Lou

Wang

2020

IEEE Trans. Neural Syst. Rehabil. Eng.

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Facial imaging and landmark detection technique for objective assessment of unilateral peripheral facial paralysis

Guo

Dai

et al. 2021

Enterprise Information Systems

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Deep Hierarchical Network With Line Segment Learning for Quantitative Analysis of Facial Palsy

2019

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We propose the deep hierarchical network (DHN) for the quantitative analysis of facial palsy. Facial palsy, also known as Bell's palsy, is the most common type of facial nerve palsy that results in the loss of muscle control in the affected facial regions. Typical symptoms include facial deformity and facial expression dysfunction. To the best of our best knowledge, all approaches for the automatic detection of facial palsy consider hand-crafted features. This paper reports the first deep-learning-based approach developed for the real-time quantitative analysis of facial palsy. The proposed DHN consists of three component networks: the first detects the subject's face, the second detects the facial landmarks and line segments on the detected face, and the third detects the local palsy regions. The first component network is built on the YOLO2 detector. The second component network is developed on a fused network architecture that incorporates a line segment learning network for locating the facial landmarks and line segments. The third component network is developed on an object detection network with the line-segment-embedded input that combines the landmarked region and the line segments detected by the second component network. The novelties of this research include: 1) the modification of a state-of-the-art edge detector for extracting the facial line segments; 2) the embedding of the line segment learning for the detection of facial landmarks and local palsy regions; 3) the quantitative description of the facial palsy syndrome intensity; and 4) the release of the first clinically labeled database, the YouTube Facial Palsy (YFP) database. The making of the YFP database solves the issue that previous methods were all evaluated on proprietary databases, making the comparison of different methods extremely difficult. The YFP database includes 32 videos of 21 patients collected from YouTube and labeled by clinic specialists. To enhance the robustness against facial expression variations, we include the CK+ facial expression database in the training. We show that the proposed DHN not only just detects the local palsy regions but also captures the intensity of the facial palsy syndrome over time, enabling the quantitative description of the syndrome. The experiments show that the proposed approach offers an accurate and efficient real-time solution for facial palsy analysis.INDEX TERMS Facial palsy, bell palsy, medical image diagnosis, face alignment, face recognition.

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Quantitative analysis of facial paralysis based on limited-orientation modified circular Gabor filters

Cited by 13 publications

References 9 publications

A Review on Automated Facial Nerve Function Assessment From Visual Face Capture

A Review on Automated Facial Nerve Function Assessment From Visual Face Capture

Facial imaging and landmark detection technique for objective assessment of unilateral peripheral facial paralysis

Deep Hierarchical Network With Line Segment Learning for Quantitative Analysis of Facial Palsy

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