Gait recognition method of temporal–spatial HOG features in critical separation of Fourier correction points

Guanqun, Liu; Zhong, Shaohui; Li, Ting

doi:10.1016/j.future.2018.09.012

Cited by 10 publications

(8 citation statements)

References 2 publications

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“…In [25], a Gabor wavelets‐based gait recognition algorithm was presented, which utilized a two‐dimensional principal component analysis method to reduce the extracted gait features. In [26], a multi‐shot gait recognition algorithm based on two‐sided Fourier correction motion point estimation was proposed, which was based on temporal‐spatial HOG feature template matching.…”

Section: Related Workmentioning

confidence: 99%

Gait recognition based on sparse linear subspace

Wen

Wang

2021

IET Image Processing

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Gait recognition has broad application prospects in intelligent security monitoring. However, due to the variability of human walking states and the complexity of external conditions during sample collection, gait recognition is still facing many challenges. Among them, gait recognition algorithms based on shallow learning are hard to achieve the correct recognition rate required by many applications, while the amount of gait training data cannot meet the needs of model training based on deep learning. To solve the above problem, this paper presents a novel gait recognition scheme based on sparse linear subspace. First, frame-by-frame gait energy images (ffGEIs) are extracted as primary gait features and sparse linear subspace technology is used to represent them for dimension reduction. Second, a new gait classification algorithm based on support vector machine is presented, which adopts Gaussian radial basis function (RBF) kernels to achieve cross-view gait recognition. Finally, the proposed gait recognition approach is evaluated on two open-accessed gait databases to demonstrate its performance.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Related Workmentioning

confidence: 99%

Gait recognition based on sparse linear subspace

Wen

Wang

2021

IET Image Processing

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“…Using tensor representation, the authors in [36] proposed an approach for cross-view gait recognition. Spatiotemporal HOG features are used also for cross-view gait recognition in [25].…”

Section: Literature Reviewmentioning

confidence: 99%

Gait recognition for person re-identification

et al. 2020

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Person re-identification across multiple cameras is an essential task in computer vision applications, particularly tracking the same person in different scenes. Gait recognition, which is the recognition based on the walking style, is mostly used for this purpose due to that human gait has unique characteristics that allow recognizing a person from a distance. However, human recognition via gait technique could be limited with the position of captured images or videos. Hence, this paper proposes a gait recognition approach for person re-identification. The proposed approach starts with estimating the angle of the gait first, and this is then followed with the recognition process, which is performed using convolutional neural networks. Herein, multitask convolutional neural network models and extracted gait energy images (GEIs) are used to estimate the angle and recognize the gait. GEIs are extracted by first detecting the moving objects, using background subtraction techniques. Training and testing phases are applied to the following three recognized datasets: CASIA-(B), OU-ISIR, and OU-MVLP. The proposed method is evaluated for background modeling using the Scene Background Modeling and Initialization (SBI) dataset. The proposed gait recognition method showed an accuracy of more than 98% for almost all datasets. Results of the proposed approach showed higher accuracy compared to obtained results of other methods result for CASIA-(B) and OU-MVLP and form the best results for the OU-ISIR dataset.

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“…In contrast, if irrelevant features are evaluated, then the system performance will go down and it will not give optimal recognition results [ 10 ]. In past, various types of features are used like shape-based features [ 15 ], geometrical features [ 16 ], and statistical features [ 17 ]. Deep features are extracted using deep convolutional neural network techniques to overcome these challenges.…”

Section: Introductionmentioning

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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Gait recognition method of temporal–spatial HOG features in critical separation of Fourier correction points

Cited by 10 publications

References 2 publications

Gait recognition based on sparse linear subspace

Gait recognition based on sparse linear subspace

Gait recognition for person re-identification

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

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