Selfgait: A Spatiotemporal Representation Learning Method for Self-Supervised Gait Recognition

Abstract

Gait recognition plays a vital role in human identification since gait is a unique biometric feature that can be perceived at a distance. Although existing gait recognition methods can learn gait features from gait sequences in different ways, the performance of gait recognition suffers from insufficient labeled data, especially in some practical scenarios associated with short gait sequences or various clothing styles. It is unpractical to label the numerous gait data. In this work, we propose a self-supervised gait recognition method, termed SelfGait, which takes advantage of the massive, diverse, unlabeled gait data as a pre-training process to improve the representation abilities of spatiotemporal backbones. Specifically, we employ the horizontal pyramid mapping (HPM) and micro-motion template builder (MTB) as our spatiotemporal backbones to capture the multi-scale spatiotemporal representations. Experiments on CASIA-B and OU-MVLP benchmark gait datasets demonstrate the effectiveness of the proposed SelfGait compared with four state-of-the-art gait recognition methods. The source code has been released at https://github.com/EchoItLiu/SelfGait.

Gait Recognition with Self-Supervised Learning of Gait Features Based on Vision Transformers

“…In this section, the results of conducted experiments are presented. It is worth noting that, except SelfGait [ 36 ], which uses self-supervised learning, every other method compared uses a supervised learning approach. Furthermore, the state-of-the-art methods mentioned in this section use silhouettes as input data, as well as features extracted directly from frames of a subject walking, while the method proposed by Liao et al [ 27 ] uses GEIs, the same as our method.…”

“…Our method performs well across all angles—specifically, the and angles—while the lowest accuracy is at an angle of . The method SelfGait [ 36 ] also uses the self-supervised learning approach but with a specialized backbone network that enhances the spatio-temporal ability of the model, and it achieves the state-of-the-art result on this dataset. In contrast, our approach uses a standard unmodified ViT network, with simple FCNN as a classifier, and achieves comparable accuracy.…”

“…Additionally, we resize all images from both datasets to size 64 × 44 as performed in [ 3 , 36 ], to ensure comparison compatibility as well as lowering computing requirements for training the DINO model. Furthermore, when training the DINO model, the training data are normalized using the and calculated from the used training data.…”

“…Finally, the model is fine-tuned in a supervised manner to the target datasets and evaluated. Another SSL approach to the gait recognition task is SelfGait [ 36 ]. In the aforementioned manuscript, the authors propose using the SSL approach for learning the spatio-temporal gait representation from unlabeled samples.…”

Gait-ViT: Gait Recognition with Vision Transformer

“…Deep learning methods [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ] have garnered a great deal of interest among researchers. Deep learning algorithms can deliver high performance without the need for feature engineering, which is time-saving.…”