Facial Expression Recognition Method Based On Lightweight Convolutional Neural Network

Facial expression recognition is an important research topic in the fields of humancomputer interaction,pattern recognition,computer vision,and intelligent automatic systems.The rise of deep neural networks provides new opportunities for the research of high-precision facial expression recognition.Taking texture information mining and correlation as the guildance and utilizing visual information intelligent processing as the stratege,a lightweight convolutional neural network facial expression recognition model is constructed.The research is of significant importance for opening out the key and basic questions,such as eature extraction under complex environmental conditions,and promoting the research of 2D target recognition theory and method in-depth.By means of lightweight technology,the highprecision recognition results can will be obtained and it have important application reference value in artificial intelligence field.The facial expression in the natural environment exsits posture,lighting variation and occlusion caused by accessories and surroundings,therefore facial expression recognition in wild is more difficult than in laboratory.The traditional framework of facial expression recognition is face detection,face alignment,feature extraction and emotion recognition.According to the reported literature,the improvement of recognition accuracy of spontaneous facial expression is very limited.Recently,driven by the development of the field of deep learning,the research of spontaneous facial expression recognition has made some breakthroughs.Compared with traditional machine learning and hand-desighed feature methods,deep learning method simulates the hierarchical system of the human visual nervous system.Due to containing more hidden layers,the deep model can extract high level and abstract features by nonlinearly transforming the original data layer by layer.High-level feature can strengthen the ability to distinguish input data,and weaken the effects of unrelated factors.However,the deep model's ability to represent non-linear functions does not represent its learnability.The construction of complexity sample for deep learning is still a challenge.In addition,the improvement of deep learning target recognition capabilities is based on deep network structure and massive training data.Therefore,accelerating the model's convergence speed and exploring effective lightweight optimization techniques are also topic worth researching.Aiming at the key and difficult problems such as feature mining and classifiers design,the facial expression images will be used in this paper to carry out research of spontaneous facial expression recognition based on lightweight convolutional neural networks.The welldistributed dataset will be obtained based on construction on naturalistic characteristic view and AutoAugmentation method with multi-channel input technique.Taking the texture information extraction and recognition mechanism as guidance,a deep convolutional neural network model for facial expression recognition is constructed.At the same time,a novel lightweight convolutional neural network model is proposed.This paper verified the validity of proposed method on the benchmark datasets,like FER2013,FERplus,CK +,SFEW and RAF-DB.