# Person Identification from Partial Gait Cycle Using Fully Convolutional Neural Network

arXiv: https://arxiv.org/abs/1804.08506 · HTML: https://mlsanity.com/paper/1804.08506/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2018.

## Built on by (most cited first, 4 of 4)

- [A Comprehensive Survey on Deep Gait Recognition: Algorithms, Datasets and Challenges](https://mlsanity.com/paper/2206.13732/) (2022)
- [Gait Recognition Based on Deep Learning: A Survey](https://mlsanity.com/paper/2201.03323/) (2022)
- [BGaitR-Net: Occluded Gait Sequence reconstructionwith temporally constrained model for gait recognition](https://mlsanity.com/paper/2110.09564/) (2021)
- [Gait Cycle Reconstruction and Human Identification from Occluded Sequences](https://mlsanity.com/paper/2206.13395/) (2022)

## Abstract (arXiv)

Gait as a biometric property for person identification plays a key role in video surveillance and security applications. In gait recognition, normally, gait feature such as Gait Energy Image (GEI) is extracted from one full gait cycle. However in many circumstances, such a full gait cycle might not be available due to occlusion. Thus, the GEI is not complete giving rise to a degrading in gait-based person identification rate. In this paper, we address this issue by proposing a novel method to identify individuals from gait feature when a few (or even single) frame(s) is available. To do so, we propose a deep learning-based approach to transform incomplete GEI to the corresponding complete GEI obtained from a full gait cycle. More precisely, this transformation is done gradually by training several auto encoders independently and then combining these as a uniform model. Experimental results on two public gait datasets, namely OULP and Casia-B demonstrate the validity of the proposed method in dealing with very incomplete gait cycles.