GaitSlice: A gait recognition model based on spatio-temporal slice features

Li, Huakang; Qiu, Yidan; Zhao, Huimin; Jin, Zuanming; Chen, Rongjun; Wei, Tuanjie; Huang, Zhihui

doi:10.1016/j.patcog.2021.108453

Cited by 42 publications

(12 citation statements)

References 21 publications

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“…The authors in [ 35 ] improved the average accuracy and reached to 84.2% that was later further improved by [ 36 ] of 87.5%. Recently, the authors of [ 37 ] obtained an average accuracy of 89.66% on the CASIA B dataset that is improved then the previous noted techniques. Our method achieved an accuracy of 91.20% on the CASIA B dataset that is improved than the existing techniques.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Human Gait Analysis: A Sequential Framework of Lightweight Deep Learning and Improved Moth-Flame Optimization Algorithm

Khan

Arshad

Damaševičius

et al. 2022

Computational Intelligence and Neuroscience

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Human gait recognition has emerged as a branch of biometric identification in the last decade, focusing on individuals based on several characteristics such as movement, time, and clothing. It is also great for video surveillance applications. The main issue with these techniques is the loss of accuracy and time caused by traditional feature extraction and classification. With advances in deep learning for a variety of applications, particularly video surveillance and biometrics, we proposed a lightweight deep learning method for human gait recognition in this work. The proposed method includes sequential steps–pretrained deep models selection of features classification. Two lightweight pretrained models are initially considered and fine-tuned in terms of additional layers and freezing some middle layers. Following that, models were trained using deep transfer learning, and features were engineered on fully connected and average pooling layers. The fusion is performed using discriminant correlation analysis, which is then optimized using an improved moth-flame optimization algorithm. For final classification, the final optimum features are classified using an extreme learning machine (ELM). The experiments were carried out on two publicly available datasets, CASIA B and TUM GAID, and yielded an average accuracy of 91.20 and 98.60%, respectively. When compared to recent state-of-the-art techniques, the proposed method is found to be more accurate.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Human Gait Analysis: A Sequential Framework of Lightweight Deep Learning and Improved Moth-Flame Optimization Algorithm

Khan

Arshad

Damaševičius

et al. 2022

Computational Intelligence and Neuroscience

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“…GaitSlice has combined residual frame attention mechanism (RFAM) with inter-related slice features to form the spatiotemporal information. The experimental results show higher accuracy than six typical gait recognition algorithms [6]. Han et al tuned the learning metrics of the gait recognition model on CASIA-B and TUM-GAID gait datasets to improve the recognition model's performance.…”

Section: Related Workmentioning

confidence: 99%

“…Due to the potential and advantages of gait identification considered algorithms than other biometrics, it uses in a wide range of security applications concerning hospitals, trade malls, banks, military installations, airports, religious institutions, etc. Moreover, it can use in crime prevention, forensic identification, and criminal investigation [ 6 ]. Despite the mentioned advantages of gait recognition, it has some drawbacks caused due to changes in clothes or carrying objects of subjects [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

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Model-based and model-free deep features fusion for high performed human gait recognition

Yousef¹,

Khalil

Samra

et al. 2023

J Supercomput

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In the last decade, the need for a non-contact biometric model for recognizing candidates has increased, especially after the pandemic of COVID-19 appeared and spread worldwide. This paper presents a novel deep convolutional neural network (CNN) model that guarantees quick, safe, and precise human authentication via their poses and walking style. The concatenated fusion between the proposed CNN and a fully connected model has been formulated, utilized, and tested. The proposed CNN extracts the human features from two main sources: (1) human silhouette images according to model-free and (2) human joints, limbs, and static joint distances according to a model-based via a novel, fully connected deep-layer structure. The most commonly used dataset, CASIA gait families, has been utilized and tested. Numerous performance metrics have been evaluated to measure the system quality, including accuracy, specificity, sensitivity, false negative rate, and training time. Experimental results reveal that the proposed model can enhance recognition performance in a superior manner compared with the latest state-of-the-art studies. Moreover, the suggested system introduces a robust real-time authentication with any covariate conditions, scoring 99.8% and 99.6% accuracy in identifying casia (B) and casia (A) datasets, respectively.

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“…There are also plenty of works utilizing the principle of ST-GCN to accomplish the recognition tasks by graph convolutional networks from 2018. In addition, we found an increasing number of works focusing on human motion recognition tasks, including gait, gestures, and skeleton behavior recognition [44], [45]. Until recent years, some works have also mentioned unsupervised and supervised deep learning methods for behavior recognition tasks.…”

Section: B Timeline Of Related Researchesmentioning

confidence: 99%

Deep Neural Networks in Video Human Action Recognition: A review

Wang¹,

Zheng²,

yang³

et al. 2023

Preprint

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<p>Currently, spatial-temporal behavior recognition is one of the most foundational tasks of computer vision. The 2D neural networks of deep learning are built for recognizing pixel-level information such as images with RGB, RGB-D, or optical flow formats, with the current increasingly wide usage of surveillance video and more tasks related to human action recognition. There are increasing tasks requiring temporal information for frames dependency analysis. The researchers have widely studied video-based recognition rather than image-based(pixel-based) only to extract more informative elements from geometry tasks. Our current related research addresses multiple novels proposed research works and compares their advantages and disadvantages between the derived deep learning frameworks rather than machine learning frameworks. The comparison happened between existing frameworks and datasets, which are video format data only. Due to the specific properties of human actions and the increasingly wide usage of deep neural networks, we collected all research works within the last three years between 2020 to 2022. In our article, the performance of deep neural networks surpassed most of the techniques in the feature learning and extraction tasks, especially video action recognition.</p>

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GaitSlice: A gait recognition model based on spatio-temporal slice features

Cited by 42 publications

References 21 publications

Human Gait Analysis: A Sequential Framework of Lightweight Deep Learning and Improved Moth-Flame Optimization Algorithm

Human Gait Analysis: A Sequential Framework of Lightweight Deep Learning and Improved Moth-Flame Optimization Algorithm

Model-based and model-free deep features fusion for high performed human gait recognition

Deep Neural Networks in Video Human Action Recognition: A review

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