Search

Your search keyword '"Chen, Gan"' showing total 46 results
Start Over Author "Chen, Gan" Publisher ieee
46 results on '"Chen, Gan"'

2. A Region Group Adaptive Attention Model For Subtle Expression Recognition.

Author

Chen, Gan, Peng, Junjie, Zhang, Wenqiang, Huang, Kanrun, Cheng, Feng, Yuan, Haochen, and Huang, Yansong

Abstract

Facial expression recognition has received extensive attention in recent years due to its important applications in many fields. Most expression samples used in research are relatively easy to analyze emotions because they have explicit expressions with strong intensities. However, in situations such as video question and answer, business negotiation, polygraph detection in the security field, autism treatment and medical escort, emotions are expressed in suppressed manners with low intensive expression or subtle expressions, making it difficult to estimate emotions accurately. In these situations, how to effectively extract expression features from facial expression images is a critical problem that affects the accuracy of subtle expression recognition. To address this problem, we propose an end-to-end group adaptive attention model for subtle expression recognition. Cropping an image into several regions of interest (ROI) according to the correlations between facial skeleton and emotions, the proposed model analyses the relationship among regions of interest, and mutual relations between local regions and the holistic region. Using the region group adaptive attention mechanism, the model effectively trains the convolutional neural network to efficiently extract facial expressions representing features and increases the accuracy and robustness of the recognition, particularly in some subtle facial expression circumstances. To improve the ability of different regional features to discriminate expressions, a group adaptive loss function is introduced to verify and improve estimation accuracy. Extensive experiments are conducted on the existing public face datasets CK+, JAFFE, KDEF and the self-collected subtle expression dataset SFER. Results show that the proposed model achieves accuracies of 99.59%, 95.20%, and 93.47% with datasets CK+, JAFFE, and KDEF, respectively. The proposed model thus generally achieves better performance in facial expression recognition than other methods. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

45. Tracking control for 2DOF helicopter via robust LQ control with adaptive law.

Author

Watanabe, Yusuke, Takami, Isao, and Chen, Gan

Abstract

In this study, a robust LQ control system with Model Reference Adaptive Control (MRAC) law is discussed. By adding MRAC law to the usual robust LQ control system, it is expected that the system output tracks to the reference signal even if there exist larger parameter perturbations that are not guaranteed in the robust LQ controller synthesis process. Through tracking to the reference signal, we expect that proposed system improves the robust control performance. The proposed system is synthesized by two-step approach. It is well known that if a controller is synthesized for a system with parameter perturbations, the tracking performance of the closed loop system is not as good as that of the closed loop with the nominal plant and a controller synthesized for the nominal plant. For this reason, a robust LQ controller is synthesized for a small perturbation, firstly. Then MRAC low is adopted for the closed loop with the plant and the robust LQ controller to improve the tracking performance when there exist large perturbations. LMI based stability analysis method for MIMO system is developed after Yang et al's method[6]. The effectiveness of the proposed method is verified by some experiments using a test-scale 2 Degree-of-Freedom (2DOF) helicopter. [ABSTRACT FROM PUBLISHER]

Published
2012

Catalog

Books, media, physical & digital resources
See catalog results