Shixin Cen
Hebei University of Technology
11 Papers
6 Citations
Shixin Cen is an academic researcher from Hebei University of Technology. The author has contributed to research in topics: Computer science & Binary pattern. The author has an hindex of 3, co-authored 4 publications.
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Papers
Spatiotemporal Feature Descriptor for Micro-Expression Recognition Using Local Cube Binary Pattern
TL;DR: A new Local Cubes Binary Patterns method for micro-expression recognition that is able to preserve the spatiotemporal information and the low feature dimension, and applies a differential calculation energy map to find regions of interest for getting a weighted energy map.
Sparse Spatiotemporal Descriptor for Micro-Expression Recognition Using Enhanced Local Cube Binary Pattern.
TL;DR: A sparse spatiotemporal descriptor for micro-expression recognition is developed by using the Enhanced Local Cube Binary Pattern (Enhanced LCBP), and the sufficiency and effectiveness of the proposed method are proved.
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Multi-task Facial Activity Patterns Learning for micro-expression recognition using Joint Temporal Local Cube Binary Pattern
TL;DR: Wang et al. as mentioned in this paper proposed a facial activity patterns learning-based micro-expression recognition method to explore the relationship between action units and emotional states, which consists of two components, i.e., Joint Temporal Local Cube Binary Pattern (Joint Temporal LCBP) and Multi-task Facial Activity Patterns Learning framework.
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Single Image Rain Removal Based on Deep Learning and Symmetry Transform
TL;DR: Experimental results show that the proposed algorithm achieves the highest value in both peak signal-to-noise ratio (PSNR) and structural similarity, which shows that the image effect of the algorithm is better after rain removal.
MCGCN: Multi-Correlation Graph Convolutional Network for Pedestrian Attribute Recognition
Yang Yu,Longlong Liu,Ye Zhu,Shixin Cen,Yang Li +4 more
- 01 Mar 2024
TL;DR: This paper proposes MCGCN, a multi-correlation graph convolutional network for pedestrian attribute recognition, which integrates semantic and visual graphs to learn regional and semantic correlations, achieving state-of-the-art performance on three benchmark datasets.
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