# View-Invariant Gait Recognition with Attentive Recurrent Learning of Partial Representations

arXiv: https://arxiv.org/abs/2010.09092 · HTML: https://mlsanity.com/paper/2010.09092/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2020.

## Built on by (most cited first, 12 of 12)

- [Deep Gait Recognition: A Survey](https://mlsanity.com/paper/2102.09546/) (2021)
- [A Comprehensive Survey on Deep Gait Recognition: Algorithms, Datasets and Challenges](https://mlsanity.com/paper/2206.13732/) (2022)
- [Generalized Inter-class Loss for Gait Recognition](https://mlsanity.com/paper/2210.06779/) (2022)
- [GaitMPL: Gait Recognition with Memory-Augmented Progressive Learning](https://mlsanity.com/paper/2306.04650/) (2023)
- [GaitFormer: Learning Gait Representations with Noisy Multi-Task Learning](https://mlsanity.com/paper/2310.19418/) (2023)
- [Sequential convolutional network for behavioral pattern extraction in gait recognition](https://mlsanity.com/paper/2104.11473/) (2021)
- [Spatiotemporal Multi-scale Bilateral Motion Network for Gait Recognition](https://mlsanity.com/paper/2209.12364/) (2022)
- [Unsupervised Gait Recognition with Selective Fusion](https://mlsanity.com/paper/2303.10772/) (2023)
- [InvKA: Gait Recognition via Invertible Koopman Autoencoder](https://mlsanity.com/paper/2309.14764/) (2023)
- [GaitASMS: Gait Recognition by Adaptive Structured Spatial Representation and Multi-Scale Temporal Aggregation](https://mlsanity.com/paper/2307.15981/) (2023)
- [GaitPoint+: A Gait Recognition Network Incorporating Point Cloud Analysis and Recycling](https://mlsanity.com/paper/2404.10213/) (2024)
- [Local Spatiotemporal Convolutional Network for Robust Gait Recognition](https://mlsanity.com/paper/2605.14548/) (2026)

## Abstract (arXiv)

Gait recognition refers to the identification of individuals based on features acquired from their body movement during walking. Despite the recent advances in gait recognition with deep learning, variations in data acquisition and appearance, namely camera angles, subject pose, occlusions, and clothing, are challenging factors that need to be considered for achieving accurate gait recognition systems. In this paper, we propose a network that first learns to extract gait convolutional energy maps (GCEM) from frame-level convolutional features. It then adopts a bidirectional recurrent neural network to learn from split bins of the GCEM, thus exploiting the relations between learned partial spatiotemporal representations. We then use an attention mechanism to selectively focus on important recurrently learned partial representations as identity information in different scenarios may lie in different GCEM bins. Our proposed model has been extensively tested on two large-scale CASIA-B and OU-MVLP gait datasets using four different test protocols and has been compared to a number of state-of-the-art and baseline solutions. Additionally, a comprehensive experiment has been performed to study the robustness of our model in the presence of six different synthesized occlusions. The experimental results show the superiority of our proposed method, outperforming the state-of-the-art, especially in scenarios where different clothing and carrying conditions are encountered. The results also revealed that our model is more robust against different occlusions as compared to the state-of-the-art methods.