Convolutional Neural Networks for Facial Expression Recognition with Few Training Samples
作者单位：School of AutomationChina University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems
会议名称：《第37届中国控制会议》
会议日期：2018年
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported by National Natural Science Foundation(NNSF)of China under Grant 61503349 and 61403422
关 键 词：Facial Expression Recognition Human-machine Interaction Convolutional Neural Network
摘 要：Facial expression recognition（FER） plays an important role in human-machine interaction. An assistant robot having a close interaction with human being should be able to recognize human facial expression. FER is a non-trivial problem because each individual has his own way to reveal his emotion and the facial expressions of two different persons may not be totally identical. Hence,facial expression recognition is still a challenging problem in computer vision. In this work, we propose a simple solution for facial expression recognition that uses a combination of Convolutional Neural Network and specific image pre-processing *** experiments employed to evaluate our technique were carried out using two largely used public databases（CK+, JAFFE）.A study of the impact of each image pre-processing operation in the accuracy rate is presented. The proposed method: achieves competitive results when compared with other facial expression recognition methods-97.85% of accuracy in the CK+ database-it is fast to train,and it allows for real time facial expression recognition with standard computers.