Comparative Study of Deep Learning Algorithms for the Detection of Facial Paralysis

Hossain, Syed Maaher; Jamal, Zeeshan; Noshin, Aurik Anjum; Khan, Mohammad Monirujjaman

doi:10.1109/iemcon56893.2022.9946491

Cited by 1 publication

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, we intend to trigger the signs that a patient may have the disease as early as possible so that the patient can be further diagnosed by a medical professional, which will facilitate the potential patient receiving treatment earlier. Recently there are many new approaches [1], [2], [3], [4] extracted facial appearances with high-level semantic features as input to the classifier via popular convolutional neural networks in an end-toend model. However, while these methods extracted coarse facial representations with the help of robust convolutional neural networks, they lacked fine-grained information [49], [50] about accurate muscle regions.…”

Section: Facial Paralysis Estimationmentioning

confidence: 99%

“…Deep learning based facial analysis tasks, such as facial recognition and facial expression recognition, aim to extract facial visual features that capture the intricate facial appearance and texture information using well-crafted Convolutional Neural Networks (CNNs). Many existing methods [1], [2], [3], [4] directly extract a global facial representation from an entire face image through CNNs to perform subsequent recognition tasks. However, accurately localizing the rele-We thank Professor Brian O'reilly for sharing part of the facial paralysis dataset.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

ALGRNet: Multi-Relational Adaptive Facial Action Unit Modelling for Face Representation and Relevant Recognitions

Ge,

Jose,

Wang

et al. 2023

IEEE Trans. Biom. Behav. Identity Sci.

View full text Add to dashboard Cite

Facial action units (AUs) represent the fundamental activities of a group of muscles, exhibiting subtle changes that are useful for various face analysis tasks. One practical application in real-life situations is the automatic estimation of facial paralysis. This involves analyzing the delicate changes in facial muscle regions and skin textures. It seems logical to assess the severity of facial paralysis by combining well-defined muscle regions (similar to AUs) symmetrically, thus creating a comprehensive facial representation. To this end, we have developed a new model to estimate the severity of facial paralysis automatically and is inspired by the facial action units (FAU) recognition that deals with rich, detailed facial appearance information, such as texture, muscle status, etc. Specifically, a novel Adaptive Local-Global Relational Network (ALGRNet) is designed to adaptively mine the context of welldefined facial muscles and enhance the visual details of facial appearance and texture, which can be flexibly adapted to facial-based tasks, e.g., FAU recognition and facial paralysis estimation. ALGRNet consists of three key structures: (i) an adaptive region learning module that identifies high-potential muscle response regions, (ii) a skip-BiLSTM that models the latent relationships among local regions, enabling better correlation between multiple regional lesion muscles and texture changes, and (iii) a feature fusion&refining module that explores the complementarity between the local and global aspects of the face. We have extensively evaluated ALGRNet to demonstrate its effectiveness using two widely recognized AU benchmarks, BP4D and DISFA. Furthermore, to assess the efficacy of FAUs in subsequent applications, we have investigated their application in the identification of facial paralysis. Experimental findings obtained from a facial paralysis benchmark, meticulously gathered and annotated by medical experts, underscore the potential of utilizing identified AU attributes to estimate the severity of facial paralysis.

show abstract