Facial expression recognition with Convolutional Neural Networks: Coping with few data and the training sample order
作者：
Highlights：
• A CNN based approach for facial expression recognition.
• A set of pre-processing steps allowing for a simpler CNN architecture.
• A study of the impact of each pre-processing step in the accuracy.
• A study for lowering the impact of the sample presentation order during training.
• High facial expression recognition accuracy (96.76%) with real time evaluation.
摘要
Highlights•A CNN based approach for facial expression recognition.•A set of pre-processing steps allowing for a simpler CNN architecture.•A study of the impact of each pre-processing step in the accuracy.•A study for lowering the impact of the sample presentation order during training.•High facial expression recognition accuracy (96.76%) with real time evaluation.
论文关键词：Facial expression recognition,Convolutional Neural Networks,Computer vision,Machine learning,Expression specific features
论文评审过程：Received 30 January 2016, Revised 15 July 2016, Accepted 16 July 2016, Available online 19 July 2016, Version of Record 13 October 2016.
论文官网地址：https://doi.org/10.1016/j.patcog.2016.07.026
