S
Stan Z. Li
Researcher at Westlake University
Publications - 625
Citations - 49737
Stan Z. Li is an academic researcher from Westlake University. The author has contributed to research in topics: Facial recognition system & Computer science. The author has an hindex of 97, co-authored 532 publications receiving 41793 citations. Previous affiliations of Stan Z. Li include Microsoft & Macau University of Science and Technology.
Papers
More filters
Proceedings ArticleDOI
Face alignment using statistical models and wavelet features
TL;DR: A method in which Gabor wavelet features are used for modeling local image structure, in which the ability of W-ASM to accurately align and locate facial features is demonstrated.
Journal ArticleDOI
Explore Efficient Local Features from RGB-D Data for One-Shot Learning Gesture Recognition
Jun Wan,Guodong Guo,Stan Z. Li +2 more
TL;DR: A novel spatiotemporal feature extracted from RGB-D data, namely mixed features around sparse keypoints (MFSK) is proposed, which outperforms all currently published approaches on the challenging data of CGD, such as translated, scaled and occluded subsets.
Proceedings ArticleDOI
Object detection by labeling superpixels
TL;DR: This paper takes object detection as a multi-label superpixel labeling problem by minimizing an energy function and uses the data cost term to capture the appearance, smooth cost terms to encode the spatial context and label costterm to favor compact detection.
Journal ArticleDOI
Robust deformable and occluded object tracking with dynamic graph.
TL;DR: A dynamic graph-based tracker (DGT) is proposed to address deformation and occlusion in visual tracking in a unified framework, and shows improved performance over several state-of-the-art trackers, in various challenging scenarios.
Proceedings ArticleDOI
Pedestrian Attribute Classification in Surveillance: Database and Evaluation
TL;DR: This paper constructs an Attributed Pedestrians in Surveillance (APiS) database with various scenes, and develops an evaluation protocol for researchers to evaluate pedestrian attribute classification algorithms.