Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (19): 179-188.DOI: 10.3778/j.issn.1002-8331.2102-0296

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Facial Expression Recognition Method Using Optimized Pruning GoogLeNet

ZHANG Hongli, BAI Xiangyu   

  1. 1.Department of Educational Technology, Inner Mongolia Normal University, Huhehot 010022, China
    2.College of Computer Science, Inner Mongolia University, Huhehot 010021, China
  • Online:2021-10-01 Published:2021-09-29



  1. 1.内蒙古师范大学 教育技术系,呼和浩特 010022
    2.内蒙古大学 计算机学院,呼和浩特 010021


In order to improve the accuracy of facial expression recognition and speed up the processing, a facial expression recognition method based on optimized pruning GoogLeNet is proposed. Firstly, the GoogLeNet network is used to extract facial features, in which the concept module can deepen the learning depth, and the typical classifier is used to realize facial expression classification. Then, the global maximum pooling layer is added to improve the GoogLeNet network, and the location information of the detected target is retained. The sigmoid cross entropy is used as the training target to obtain the comprehensive facial expression feature information. Finally, the pruning algorithm is used to train the GoogLeNet network, pruning the low weight connection and retraining the network, so as to simplify the network structure and parameters and improve the operation efficiency. The proposed method is tested on the data sets of JAFFE, CK+ and Cohn Kanade. The results show that the recognition accuracy of the proposed method is 83.84%, 85.09% and 84.87% respectively, and the running time is less than 200?ms, which is better than the comparison method and has certain universality.

Key words: pruning algorithm, GoogLeNet, facial expression recognition, Inception module, global maximum pooling layer, operating efficiency



关键词: 剪枝算法, GoogLeNet, 人脸表情识别, Inception模块, 全局最大池化层, 运行效率