GEINet: View-invariant gait recognition using a convolutional neural network
By Kohei Shiraga, Yasushi Makihara, Daigo Muramatsu, Tomio Echigo, Yasushi Yagi. International Conference on Biometrics, 2016
417 citations recorded by the source corpus.
Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Human-Computer Interaction
View Paper: https://www.semanticscholar.org/paper/1a13b5dd42df52ffc89b80a133a7677aeebd788c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biomedical Engineering
- Daigo Muramatsu
- Junping Zhang
- Jianfeng Feng
- Yiwei He
- Hanqing Chao
- Tomio Echigo
- Yasushi Makihara
- Yasushi Yagi
In Computer Vision and Pattern Recognition
- Daigo Muramatsu
- Junping Zhang
- Jianfeng Feng
- Yiwei He
- Hanqing Chao
- Tomio Echigo
- Yasushi Yagi
- Yasushi Makihara
In Human-Computer Interaction
- Daigo Muramatsu
- Yasushi Makihara
- Yasushi Yagi
- Tomio Echigo
- Yongzhen Huang
- Xin Yu
- Lishan Qiao
- Jianfeng Feng
Citations in the corpus
- Pattern Recognition (2018, positive): “Different from the relatively shallow networks in [5,30,41] , we propose very deep convolutional neural networks.”
- Gait Recognition via Effective Global-Local Feature Representation and Local Temporal Aggregation (2020, methodology): “Table 4 desplays the comparison results of our method and several famous algorithms, including GEINet[18], GaitSet[3], GaitPart[5] and GLN[9].”
- Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition (2018, methodology): “On the other hand, OUISIR gait database, large population (OU-LP) [22] used in [17, 19, 20], contains gait images of a large population (4016 subjects) but its range of view angle is narrow (4 views, 55°, 65°, 75°, 85°).”
- GaitPart: Temporal Part-Based Model for Gait Recognition (2020, background): “GEINet:[18], GaitSet:[5].”
- GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition (2018, methodology): “Third, they extract the representation of the gait by machine learning approaches such as Canonical Correlation Analysis (CCA) (Xing et al. 2016), Linear Discriminant Analysis (LDA) (Bashir, Xiang, and Gong 2010) and deep learning (Shiraga et al. 2016).”