Facial Paralysis Recognition And Grading Assessment Based On Deep Differentiated Network

Facial paralysis is a common disease of facial expression muscles dysfunction and has a high incidence.However,there are relatively few professional doctors for the diagnosis and treatment of facial paralysis,and doctors are largely influenced by subjective factors in the diagnosis process,resulting in the difficulty for patients with facial paralysis to get timely and accurate diagnosis,which is not conducive to the subsequent treatment of patients.Automatic identification and degree evaluation for facial paralysis based on computer vision technology is a fast and effective means for assisting facial paralysis.It helps doctors to diagnose the patient's condition to achieve a more reasonable treatment plan.Therefore,the use of computer vision technology for automatic identification and evaluation of facial paralysis is of great significance for the diagnosis and treatment of facial paralysis.At present,although there are many methods for facial paralysis recognition and evaluation,these methods are all based on facial asymmetry,while normal people's faces could also have natural asymmetry when they make some expression or hold still.Secondly,most existing methods use shallow models,and mainly focus on some single shallow features of facial paralysis,etc.It is difficult for them to comprehensively and effectively identify and evaluate facial paralysis,and the methodology needs to be further improved.Based on the existing facial recognition and evaluation methods,this paper focuses on the above problems and uses the methods and theories of deep learning to carry out the following new research works:1.For the identification of facial paralysis,when the suspected patient repeats a diagnostic facial action,normal people with abnormality or asymmetry on their faces usually exhibit larger difference than real patients.Accordingly,this paper proposes a novel recognition method based on the so-called Deep Differentiated Network(DDN)for facial paralysis recognition.Rather than extracting differentiated features from raw facial images,or from extracted facial features,DDN is a network architecture that jointly optimizes high-level feature extraction and differentiated feature calculation.It uses two-stream Convolution Neural Network(CNN)to extract the facial states of the suspected patients when they make the same action at two different times.Then the differentiated features between the two trials can be obtained by a single branch convolution network,and facial paralysis recognition can be performed based on the final differentiated features.Experimental results show that DDN can effectively identify whether the suspected patient has facial paralysis,and the recognition accuracy rate is 89.67%.2.In view of the assessment of the severity of facial paralysis,existing methods often ignore facial motion information,and rely on shallow-structured machine learning models which may not well extract effective features of facial states.To this end,this paper proposes a facial paralysis degree evaluation method based on Deep Differentiated Network and LSTM(DDN-LSTM).The design principle is based on the diagnosis results depending on the facial asymmetry and muscle movement ability of the patient during facial diagnosis.The differences in the sides of the patient's face are analyzed by using a Deep Differentiated Network,and then the LSTM network is used to extract the motion features.Finally,the extracted high-level features are used for facial paralysis degree evaluation.The experimental results show that DDN-LSTM is superior to state-of-art baseline methods in evaluating facial paralysis.