S
Shiqi Yu
Researcher at Southern University of Science and Technology
Publications - 76
Citations - 3917
Shiqi Yu is an academic researcher from Southern University of Science and Technology. The author has contributed to research in topics: Gait (human) & Feature extraction. The author has an hindex of 23, co-authored 64 publications receiving 2563 citations. Previous affiliations of Shiqi Yu include Shenzhen University & The Chinese University of Hong Kong.
Papers
More filters
Proceedings ArticleDOI
A Framework for Evaluating the Effect of View Angle, Clothing and Carrying Condition on Gait Recognition
Shiqi Yu,Daoliang Tan,Tieniu Tan +2 more
TL;DR: A framework consisting of a large gait database, a large set of well designed experiments and some evaluation metrics to evaluate gait recognition algorithms is proposed.
Journal ArticleDOI
Convolutional neural networks for hyperspectral image classification
Shiqi Yu,Sen Jia,Chunyan Xu +2 more
TL;DR: An efficient CNN architecture has been proposed to boost its discriminative capability for hyperspectral image classification, in which the original data is used as the input and the final CNN outputs are the predicted class-related results.
Journal ArticleDOI
A Study on Gait-Based Gender Classification
TL;DR: The prior knowledge extracted from the psychological experiments can be combined with an automatic method to further improve classification accuracy, and the proposed method achieves higher performance than some other methods, and is even more accurate than human observers.
Journal ArticleDOI
A model-based gait recognition method with body pose and human prior knowledge
TL;DR: PoseGait exploits human 3D pose estimated from images by Convolutional Neural Network as the input feature for gait recognition and design spatio-temporal features from the3D pose to improve the recognition rate.
Journal ArticleDOI
A survey: Deep learning for hyperspectral image classification with few labeled samples
TL;DR: Although there is a vast gap between deep learning models (that usually need sufficient labeled samples) and the HSI scenario with few labeled samples, the issues of small-sample sets can be well characterized by fusion of deep learning methods and related techniques, such as transfer learning and a lightweight model.