GAITTAKE: Gait Recognition by Temporal Attention and Keypoint-Guided Embedding

Hsu, Hung-Min; Wang, Yizhou; Yang, Cheng-Yen; Hwang, Jau-Chung; Thuc, Hoang Le Uyen; Kim, Kwang-Ju

doi:10.1109/icip46576.2022.9897409

Cited by 14 publications

(4 citation statements)

References 19 publications

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“…Probe Angle NM #5-6 0°90°180°G aitSet [24] 90.8% 91.7% 85.8% 3DLocal [79] 96.0% 94.2% 95.2% CSTL [74] 97.8% 95.2% 96.7% GLFE+GLConv [10] 96.0% 95.4% 94.0% GaitGraph [64] 85.3% 86.5% 81.9% GaitSet [23] 91.1% 94.5% 88.0% STC-Att [67] 97.0% 97.3% 96.0% GaitTAKE [27] 96.7% 96.3% 96.4% LagrangeGait [62] 95.2% 94.6% 91.5% RCC-PFL [75] 87.4% 82.6% 80.3% Siamese (ours) 95% 100% 98.90%…”

Section: Table 2 a Comparison Between Related Work Results On Casia-b...mentioning

confidence: 99%

“…Hung-Min et al [27], Liao et al [48], Tianrui et al [62], Shopon et al [40] and Muhammad et al [36]:…”

Section: Related Work a Human Gait Recognitionmentioning

confidence: 99%

“…Hung-Min et al [27] proposed a gait recognition framework called Temporal Attention and Keypoint-guided Embedding (GaitTAKE). The framework merges the global appearance feature with the local appearance feature based on temporal attention and a temporal aggregated human pose feature.…”

Section: Related Work a Human Gait Recognitionmentioning

confidence: 99%

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Biometric-Based Security System for Smart Riding Clubs

et al. 2022

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Horses, workers or riders need safety in a farm or a riding club. On account of the great value of the horse, the breeder needs to protect it from theft and disease. In this context, it is important to detect and recognize the identity of each worker or rider and horse for security reasons. In fact, this paper proposes a Smart Riding Club Biometric System (SRCBS) consisting in automatically detecting and recognizing horses as well as humans. The proposed system is based on the facial biometrics for a horse as well as the human gait biometrics due to their simplicity and intuitiveness in an uncontrolled environment. This work suggests a Siamese network based on DenseNet features for human gait recognition, a Sparse Neural Network (SNN) based on sparse features for horse face detection and a horse face recognition method based on Gabor features, LDA and SVM. Because of the unavailability of horse databases, this paper presents a new benchmark for horse detection and recognition in order to evaluate our proposed system. The proposed systems achieved an average accuracy of gait recognition equal to 95% in the 0°view, 100% in the 90°view and 98.90% in the 180°view on Casia-B dataset, an average precision equal to 90% for horse face detection and a recognition rate equal to 99.89% for horse face identification.INDEX TERMS Horse face detection, human gait recognition, neural network, siamese network, smart riding club, THDD.

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Section: Table 2 a Comparison Between Related Work Results On Casia-b...mentioning

confidence: 99%

“…Hung-Min et al [27], Liao et al [48], Tianrui et al [62], Shopon et al [40] and Muhammad et al [36]:…”

Section: Related Work a Human Gait Recognitionmentioning

confidence: 99%

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Biometric-Based Security System for Smart Riding Clubs

et al. 2022

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“…Moreover, this simplified input enables efficient and practical processing. Extensive in-the-lab experiments with shallow models have achieved impressive performance, i.e., 95.9% on CASIA-B dataset (Hsu et al 2022) and 92.1% on OU-MVLP dataset (Dou et al 2023).…”

Section: Introductionmentioning

confidence: 99%

QAGait: Revisit Gait Recognition from a Quality Perspective

Wang,

Hou,

Zhang

et al. 2024

AAAI

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Gait recognition is a promising biometric method that aims to identify pedestrians from their unique walking patterns. Silhouette modality, renowned for its easy acquisition, simple structure, sparse representation, and convenient modeling, has been widely employed in controlled in-the-lab research. However, as gait recognition rapidly advances from in-the-lab to in-the-wild scenarios, various conditions raise significant challenges for silhouette modality, including 1) unidentifiable low-quality silhouettes (abnormal segmentation, severe occlusion, or even non-human shape), and 2) identifiable but challenging silhouettes (background noise, non-standard posture, slight occlusion). To address these challenges, we revisit gait recognition pipeline and approach gait recognition from a quality perspective, namely QAGait. Specifically, we propose a series of cost-effective quality assessment strategies, including Maxmial Connect Area and Template Match to eliminate background noises and unidentifiable silhouettes, Alignment strategy to handle non-standard postures. We also propose two quality-aware loss functions to integrate silhouette quality into optimization within the embedding space. Extensive experiments demonstrate our QAGait can guarantee both gait reliability and performance enhancement. Furthermore, our quality assessment strategies can seamlessly integrate with existing gait datasets, showcasing our superiority. Code is available at https://github.com/wzb-bupt/QAGait.

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