Hui Ding

Prof. Dr. Hui Ding

Capital Normal University, Beijing, China
orcid
Guest Editor [Mathematics] Special Issue: New Advances in Me...

Research Focus & Metrics

Academic Career Length

10Years (2017-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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4 September 2023 in Expert Systems with Applications
Verified
Journal Article
Closed access
YPYuchen Pan
Yuchen Pan
YSYuanyuan Shang
Yuanyuan Shang
TLTie Liu
Tie Liu
ZSZhuhong Shao
Zhuhong Shao
GGGuodong Guo
Guodong Guo
QHQiang Hu
Qiang Hu

17 July 2023 in IEEE Transactions on Affective Computing
Verified
Journal Article
Closed access
YPYuchen Pan
Yuchen Pan
YSYuanyuan Shang
Yuanyuan Shang
Zhuhong ShaoZhuhong Shao
Zhuhong Shao
TLTie Liu
Tie Liu
GGGuodong Guo
Guodong Guo
Hui DingHui Ding
Hui Ding
Automatic depression diagnosis is a challenging problem, that requires integrating spatial-temporal information and extracting features from audio-visual signals. In terms of privacy protection, the development trend of recognition algorithms based on facial landmarks has created additional challenges and difficulties. In this paper, we propose an audio-visual attention network (AVA-DepressNet) for depression recognition. It is a novel multimodal framework with facial privacy protection, and uses attention-based modules to enhance audio-visual spatial and temporal features. In addition, an adversarial multistage (AMS) training strategy is developed to optimize the encoder-decoder structure. Additionally, facial structure prior knowledge is creatively used in AMS training. Our AVA-DepressNet is evaluated on popular audio-visual depression datasets: AVEC 2013, AVEC 2014, and AVEC 2017. The results show that our approach reaches the state-of-the-art performance or competitive results for depression recognition.

17 November 2023 in Biomedical Signal Processing and Control
Verified
Journal Article
Closed access
YPYuchen Pan
Yuchen Pan
YSYuanyuan Shang
Yuanyuan Shang
WWWei Wang
Wei Wang
ZSZhuhong Shao
Zhuhong Shao
ZHZhuojin Han
Zhuojin Han
Hui DingHui Ding
Hui Ding


4 November 2018 in Mathematical Problems in Engineering
Verified
Journal Article
Open access
PDF file
CZChunyu Zhang
Chunyu Zhang
Hui DingHui Ding
Hui Ding
YSYuanyuan Shang
Yuanyuan Shang
ZSZhuhong Shao
Zhuhong Shao
XFXiaoyan Fu
Xiaoyan Fu
For gender classification, we present a new approach based on Multiscale facial fusion feature (MS3F) to classify gender from face images. Fusion feature is extracted by the combination of Local Binary Pattern (LBP) and Local Phase Quantization (LPQ) descriptors, and a multiscale feature is generated through Multiblock (MB) and Multilevel (ML) methods. Support Vector Machine (SVM) is employed as the classifier to conduct gender classification. All the experiments are performed based on the Images of Groups (IoG) dataset. The results demonstrate that the application of Multiscale fusion feature greatly improves the performance of gender classification, and our approach outperforms the state-of-the-art techniques.

6 November 2024 in Applied Soft Computing
Verified
Journal Article
Closed access
Hui DingHui Ding
Hui Ding
YHYuhan Huang
Yuhan Huang
NCNianzhe Chen
Nianzhe Chen
JLJiacheng Lu
Jiacheng Lu
SLShao-Chun Li
Shao-Chun Li