Human Identification Using Temporal Information Preserving Gait Template
By Chen Wang, Junping Zhang, Liang Wang, Jian Pu, Xiaoru Yuan. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012
294 citations recorded by the source corpus.
Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Human-Computer Interaction
View Paper: https://www.semanticscholar.org/paper/2d01cbc84241f1542946f0bcd4ab5cf4f79015cd
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
- Tardi Tjahjadi
- Xiaoming Xiao
- Jianfeng Feng
Citations in the corpus
- The OU-ISIR Gait Database Comprising the Large Population Dataset and Performance Evaluation of Gait Recognition (2012, result): “For example, according to the latest evaluation results of the CGI reported in [45], GEI performance is superior to that of CGI only if there is no intrasubject variation and only a single gait period occurs in a sequence.”
- Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition (2018, methodology): “Though we evaluated only GEI-based methods in order to compare gait recognition methodologies, there are many gait features that can be used for input images, such as DeepGait [41], CGI (chrono-gait image) [42], and FDF (frequency-domain feature) [43].”
- Robust arbitrary view gait recognition based on parametric 3D human body reconstruction and virtual posture synthesis (2016, methodology): “In 2D gait recognition, gait silhouettes in a period are usually averaged in various ways to form GEI [10], GFI [41], CGI [42], DNGR [43] or similar gait energy features for recognition.”
- Gait recognition using wifi signals (2016, methodology): “An example camera based scheme achieves an accuracy of 91% on the USF HumanID Database with 122 subjects [29].”
- GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition (2018, methodology): “The goal of generation is to compress gait information into a single image, e.g., Gait Energy Image (GEI) (Han and Bhanu 2006) and Chrono-Gait Image (CGI) (Wang et al. 2012).”
- GaitPart: Temporal Part-Based Model for Gait Recognition (2020, methodology): “However, RNN-based methods are believed to retain unnecessary sequential constrains for the periodic gait[5], while another kind of GEI-based methods[23, 7, 3, 26] are sensitive to the variations in realworld scenarios, despite the simplicity advantage of them.”