液晶与显示2024，Vol.39Issue(4)：522-531,10.DOI:10.37188/CJLCD.2023-0153
NCA-MobileNet:一种轻量化人脸表情识别方法
NCA-MobileNet:a lightweight facial expression recognition method
摘要
Abstract
At present,facial expression recognition methods have the problems of large number of parameters,large consumption of computing resources and low recognition accuracy.Aiming at the above problems,a lightweight human facial expression recognition method based on conditional coordinated attention mechanism is studied.First,the number of layers of MobileNet V3 network is reduced,while the numbers of intermediate channels and output channels of the inverse residual structure are increased to 1.5～3.2 times of the original number.Mish is used instead of Hardswish activation function to realize the nonlinearization after feature extraction.Secondly,an improved coordinated attention mechanism is introduced to encode the tensor information embedding along horizontal and vertical directions sequentially by maximum pooling and average pooling.And tensor information integration is used to generate features with global sensory field and precise location information to extract detailed information of facial expressions in space and channel location.Finally,experiments are conducted on the publicly available datasets FERPlus and RAF-DB,and the results show that the proposed method reduces the number of parameters by 15.91%,and the accuracy rates are 88.84%and 85.90%,respectively,which are 0.83%and 1.39%higher than the accuracy rates of the model before improvement.The method has good recognition performance and validate the effectiveness of the proposed method.关键词
表情识别/轻量化/注意力机制/特征提取Key words
facial expression recognition/lightweight/attention mechanism/feature extraction分类
信息技术与安全科学引用本文复制引用
左义海,白武尚,何秋生..NCA-MobileNet:一种轻量化人脸表情识别方法[J].液晶与显示,2024,39(4):522-531,10.基金项目
山西省自然科学基金(No.20210302123222) （No.20210302123222）
山西省教学改革项目(No.J20221103)Supported by Natural Science Foundation of Shanxi Province(No.20210302123222) （No.J20221103）
Teaching Reform Project of Shanxi Province(No.J20221103) （No.J20221103）
