.png){kind=icon}
.png){kind=icon}
Return
Facial Expression Recognition With Deeply-Supervised Attention Network
.png){kind=icon}
PRE
AI
DOI：10.1109/TAFFC.2020.2988264.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Facial expression recognition (FER) is crucial for social communication. However, current studies present limitations when addressing facial expression difference due to demographic variation, such as race, gender, and age, etc. In this article, we first propose a deeply-supervised attention network (DSAN) to recognize human emotions based on facial images automatically. Based on DSAN, a two-stage training scheme is designed, taking full advantage of the race/gender/age-related information. In our DSAN framework, multi-scale features are leveraged to capture more discriminative information from the deep layers to the shallow layers. Furthermore, we adopt the attention block to highlight the essential local facial characteristics; it performs well when it is incorporated into the deeply-supervised framework. Finally, we combine the complementary characteristics of multiple convolutional layers in deeply-supervised manner and ensemble the intermediate predicted scores. Our experimental results have shown that our proposed framework can (i) effectively integrate demographic information in improving the performance of a variety of FER tasks, (ii) learn informative feature representations with a visual explanation by capturing the regions of interests (ROI), (iii) achieve superior performance for both the posed and the spontaneous FER databases, each containing pictures of human facial expressions varied in gender, age or race.
Keywords:
Training
Face
Face recognition
Task analysis
Visualization
Computer vision
Feature extraction
Facial expression recognition
demographic variation
deeply-supervised attention network
complementary characteristics
visual explanation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
9.8
Papers:
1.3K
Citations:
9.1K