ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2021, Vol. 58 ›› Issue (3): 528-538.doi: 10.7544/issn1000-1239.2021.20200288

Previous Articles     Next Articles

Robust Face Expression Recognition Based on Gender and Age Factor Analysis

Liao Haibin1,2, Xu Bin1   

  1. 1(School of Computer Science and Technology, Hubei University of Science and Technology, Xianning, Hubei 437100);2(Jiangxi Smart City Industrial Technology Research Institute, Nanchang 330096)
  • Online:2021-03-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61701174), the Xianning Municipal Natural Science Foundation (2019kj130), and the Cultivation Foundation of Hubei University of Science and Technology (202022GP03).

Abstract: A robust face expression recognition method based on deep conditional random forest is proposed to solve the problem of factors such as race, gender and age in non-controllable environment. Different from the traditional single task facial expression recognition models, we devise an effective multi-task face expression recognition architecture that is capable of learning from auxiliary attributes like gender and age. In the study, we find that facial attributes of gender and age have a great impact on facial expression recognition. In order to capture the relationship between facial attributes and facial expressions, a deep conditional random forest based on facial attributes is proposed for face expression recognition. In the feature extraction stage, multi-instance learning integrated with attention mechanism is used to extract face features to remove variations including illumination, occlusion and low resolution. In the facial expression recognition stage, according to the facial attributes of gender and age, the multi-condition random forest method is used to recognize facial expressions. A large number of experiments have been carried out on the open CK+, ExpW, RAF-DB and AffectNet face expression databases: the recognition rate reaches 99% on the normalized CK+ face database and 70.52% on the challenging natural scene database. The experimental results show that our proposed method has better performance than the state-of-the-art methods; furthermore, it is robust to occlusion, noise and resolution variation in the wild.

Key words: face expression recognition, face attribute analysis, deep learning, attention mechanism, random forest

CLC Number: