Facial expression recognition based on improved lightweight convolutional neural network MobileNetV3
The timely detection of facial expression recognition in teaching can effectively improve the quality of education and student engagement.To achieve real-time detection of facial expression recognition in teaching,this study is based on the con-volutional neural network MobileNetV3 for learning,and improves SE and convolutional layers to construct a facial expression rec-ognition model that can recognize 8 different expression categories.Studying the impact of the position of void convolutions on model performance and found that placing void convolutions at the front of the network has a positive impact on performance,while placing them at the back can lead to performance degradation.At the same time,the performance of the model was further im-proved by introducing the SSE(Space Squeeze and Extraction)module and optimizing its position and structure.The final proposed improved version of MobileNetV3 significantly reduced the number of parameters and model file size,but the accuracy decreased by about 1%.And multiple random experiments were conducted on the model,which showed good robustness.This study can pro-vide theoretical basis and technical support for the real-time application of facial expression recognition in teaching.In the future,we will focus on developing facial expression recognition systems that can be applied on mobile devices.
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