STAR: Spatio-Temporal Augmented Relation Network for Gait Recognition

Huang, Xiaohu; Wang, Xinggang; He, Botao; He, Shan; Liu, Wenyu; Feng, Bin

doi:10.1109/tbiom.2022.3211843

Cited by 11 publications

(1 citation statement)

References 41 publications

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“…Most existing gait recognition works are trained in a supervised manner, in which cross-cloth and cross-view labeled sequence pairs have been provided. They mainly focus on learning more discriminative features [1], [2], [3], [11], [12], [13], [14], [15], [16] or developing gait recognition applications in natural scenes [17], [18], [19], [20]. Despite the abundance of research in gait recognition, there is still a need for further exploration of its practical applications.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Gait Recognition with Selective Fusion

Ren¹,

Hou²,

Cao³

et al. 2023

Preprint

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Previous gait recognition methods primarily trained on labeled datasets, which require painful labeling effort. However, using a pre-trained model on a new dataset without fine-tuning can lead to significant performance degradation. So to make the pre-trained gait recognition model able to be fine-tuned on unlabeled datasets, we propose a new task: Unsupervised Gait Recognition (UGR). We introduce a new cluster-based baseline to solve UGR with cluster-level contrastive learning. But we further find more challenges this task meets. First, sequences of the same person in different clothes tend to cluster separately due to the significant appearance changes. Second, sequences taken from 0 • and 180 • views lack walking postures and do not cluster with sequences taken from other views. To address these challenges, we propose a Selective Fusion method, which includes Selective Cluster Fusion (SCF) and Selective Sample Fusion (SSF). With SCF, we merge matched clusters of the same person wearing different clothes by updating the cluster-level memory bank with a multi-cluster update strategy. And in SSF, we merge sequences taken from front/back views gradually with curriculum learning. Extensive experiments show the effectiveness of our method in improving the rank-1 accuracy in walking with different coats condition and front/back views conditions.

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Section: Related Workmentioning

confidence: 99%

Unsupervised Gait Recognition with Selective Fusion

Ren¹,

Hou²,

Cao³

et al. 2023

Preprint

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A survey of appearance-based approaches for human gait recognition: techniques, challenges, and future directions

Güner Şahan,

Şahin,

Kaya Gülağız

2024

J Supercomput

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Gait recognition has become an important biometric feature for human identification, in addition to data such as face, iris, and fingerprint. The goal of human gait recognition is to identify people based on walking images. Artificial intelligence technologies have revolutionized the field of gait recognition by enabling computers to automatically learn and extract intricate patterns. These techniques examine video recordings to determine key features in an individual's gait, and these features are used to identify the person. This paper examines the existing appearance-based gait recognition methods that have been published in recent years. The primary objective of this paper is to provide an informative survey of the state-of-the-art in appearance-based gait recognition techniques, highlighting their applications, strengths, and limitations. Through our analysis, we aim to highlight the significant advance that has been made in this field, draw attention to the challenges that have been faced, and identify areas of prospective future research and advances in technology. Furthermore, we comprehensively examine common datasets used in gait recognition research. By analyzing the latest developments in appearance-based gait recognition, our study aims to be a helpful resource for researchers, providing an extensive overview of current methods and guiding future attempts in this dynamic field.

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