TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Lightweight CNN-based Expression Recognition on Humanoid Robot |
| 영문제목(English Title) |
Lightweight CNN-based Expression Recognition on Humanoid Robot |
| 저자(Author) |
Zhenzhen Yang
Nan Kuang
Yongpeng Yang
Bin Kang
Guangzhe Zhao
Hanting Yang
Yong Tao
Lei Zhang
Chunxiao Zhao
|
| 원문수록처(Citation) |
VOL 14 NO. 03 PP. 1188 ~ 1203 (2020. 03) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
The human expression contains a lot of information that can be used to detect complex conditions such as pain and fatigue. After deep learning became the mainstream method, the traditional feature extraction method no longer has advantages. However, in order to achieve higher accuracy, researchers continue to stack the number of layers of the neural network, which makes the real-time performance of the model weak. Therefore, this paper proposed an expression recognition framework based on densely concatenated convolutional neural networks to balance accuracy and latency and apply it to humanoid robots. The techniques of feature reuse and parameter compression in the framework improved the learning ability of the model and greatly reduced the parameters. Experiments showed that the proposed model can reduce tens of times the parameters at the expense of little accuracy.
|
| 키워드(Keyword) |
multimodal image registration
self-similarity
image segmentation
symmetry detection
magnetic resonance imaging
Humanoid Robot
Human-machine interaction
CNN
Emotion Recognition
|
| 파일첨부 |
PDF 다운로드
|
