A Comprehensive Study on Cross-View Gait Based Human Identification with Deep CNNs

By Zifeng Wu, Yongzhen Huang, Liang Wang, Xiaogang Wang, Tien-Ping Tan. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017

642 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Endocrinology, Diabetes and Metabolism

View Paper: https://www.semanticscholar.org/paper/5f5164cf998a10d2bef37741adb562ab07fac413

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

1. Shiliang Zhang
2. Qi Tian
3. Yongzhen Huang
4. Junping Zhang
5. Xin Yu
6. Beibei Lin
7. Shunli Zhang
8. Manuel Jesús Marín-Jiménez

In Computer Vision and Pattern Recognition

1. Qi Tian
2. Dacheng Tao
3. Yi Yang
4. Shiliang Zhang
5. Liang Zheng
6. Yongdong Zhang
7. Bernt Schiele
8. Richang Hong

Citations in the corpus

• GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition (2018, methodology): “Based on the way of extracting temporal information, they can be classified into LSTM-based approaches (Liao et al. 2017) and 3D CNN-based approaches (Wolf, Babaee, and Rigoll 2016; Wu et al. 2017).”
• Deep Representation Learning With Part Loss for Person Re-Identification (2017, methodology): “classification [20], lots of approaches have been proposed to generate representations based on CNN [19], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30].”
• iLGaCo: Incremental Learning of Gait Covariate Factors (2020, methodology): “Existing gait methods fall into two typical categories: model-based methods [23, 24] and appearance-based methods [3, 6, 21, 26, 42].”
• Gait Recognition via Effective Global-Local Feature Representation and Local Temporal Aggregation (2020, methodology): “We compare the proposed method with the latest gait recognition methods, including ViDP [10], CMCC [13], GaitSet [3], AE [23], MGAN [7], CNN-LB, CNN-3D, CNN-Ensemble [21], ACL [27] and GaitPart [5] on CASIA-B.”
• Pattern Recognition (2018, neutral): “Note that we are different from the work in [5] though we both consider information fusion.”
• GaitPart: Temporal Part-Based Model for Gait Recognition (2020, methodology): “For the temporal changing characteristics, some of these state-of-art methods do not model temporal features explicitly, which leads to the loss of important invariant features in time series[23, 7, 3, 26].”
• Gaitgraph: Graph Convolutional Network for Skeleton-Based Gait Recognition (2021, methodology): “For a fair comparison, this paper follows the popular protocol by [5].”