Improved gait recognition through gait energy image partitioning

Premalatha, G.; Chandramani, Premanand Venkatesh

doi:10.1111/coin.12340

Cited by 11 publications

(4 citation statements)

References 20 publications

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“…Moreover, the proposed technique has been compared with the recent state-of-art studies and tabulated in Table 9, which approved that the proposed technique outperforms the current studies despite its deep structure and using many models. Still, it enhanced the accuracy value by 1.46% than [4], 0.67% than [5], 4.24% than [10], and 8.07% than [1].…”

Section: Comparative Studymentioning

confidence: 88%

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Accurate metaheuristic deep convolutional structure for a robust human gait recognition

Yousef,

Khalil,

Samra

et al. 2023

IJECE

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<span lang="EN-US">Gait recognition has become a developing technology in various security, industrial, medical, and military applications. This paper proposed a deep convolutional neural network (CNN) model to authenticate humans via their walking style. The proposed model has been applied to two commonly used standardized datasets, Chinese Academy of Sciences (CASIA) and Osaka University-Institute of Scientific and Industrial Research (OU-ISIR). After the silhouette images have been isolated from the gait image datasets, their features have been extracted using the proposed deep CNN and the traditional ones, including AlexNet, Inception (GoogleNet), VGGNet, ResNet50, and Xception. The best features were selected using genetic, grey wolf optimizer (GWO), particle swarm optimizer (PSO), and chi-square algorithms. Finally, recognize the selected features using the proposed deep neural network (DNN). Several performance evaluation parameters have been estimated to evaluate the model’s quality, including accuracy, specificity, sensitivity, false negative rate (FNR), and training time. Experiments have demonstrated that the suggested framework with a genetic feature selector outperforms previous selectors and recent research, scoring accuracy values of 99.46% and 99.09% for evaluating the CASIA and OU-ISIR datasets, respectively, in low time (19 seconds for training).</span>

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Section: Comparative Studymentioning

confidence: 88%

“…Premalatha and Chandramani [5] proposed a gait authentication technique to overcome the gait covariate factors. Their model extracted the gait features from silhouette images using a histogram of oriented gradients (HOG) algorithm and then recognized them by a K-nearest neighbor (K-NN) technique.…”

Section: Introductionmentioning

confidence: 99%

Accurate metaheuristic deep convolutional structure for a robust human gait recognition

Yousef,

Khalil,

Samra

et al. 2023

IJECE

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“…The number of channels is set according to the number of categories of the contour feature vector. Under the action of convolution kernel, the values of each channel are connected in series into a one-dimensional vector, which expresses the contour feature [19] [20]. The data in the five channels are added and summed, and input into softmax to calculate the probability results of the corresponding classification units.…”

Section: Dynamic Gait Recognition Model Based On Contour Featuresmentioning

confidence: 99%

Dynamic Gait Recognition of Chinese Dance Based on Contour Features

Gong¹

2023

Atlantis Highlights in Computer Sciences

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Single feature recognition has certain limitations, which can't fully reflect the differences between dance gait information, resulting in low recognition rate of frame difference features. In this paper, a dynamic gait recognition method for Chinese classical dance is proposed based on contour features. The gait image of Chinese dance is preprocessed, the target region is segmented, and the gait cycle is extracted. The gait energy map is composed of the sequences in a cycle, which contains the information of the upper and lower limb motion frequencies of the dancer. The key distance, width, wavelet features and gait energy map are fused to form a group of feature vectors. The problem of low efficiency of single feature recognition is solved by feature fusion. Based on CNN model, a dynamic gait recognition model of Chinese dance is established based on contour features. Under the function of convolution kernel, the values of each channel are concatenated into a one-dimensional vector to express the contour features and complete the video sequence classification. The test results show that the design method retains the correlation between the gait data of Chinese classical dance, accurately locates the key points, and reflects the movement and time information of gait, so the recognition rate is improved.

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“…G AIT recognition is a long-distance biological identification technology, which relies on the walking patterns of human beings, and now reveals great application potential on public security [1], [2], [3], [4] and identity recognition [5], [6], [7]. Although gait recognition has drawn increasing research attention recently, it remains challenging to learn discriminative temporal representation since the silhouette differences in spatial domain are quite subtle.…”

Section: Introductionmentioning

confidence: 99%

Context-Sensitive Temporal Feature Learning for Gait Recognition

Huang

Zhu

Wang

et al. 2021

2021 IEEE/CVF International Conference on Computer Vision (ICCV)

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Although gait recognition has drawn increasing research attention recently, it remains challenging to learn discriminative temporal representation, since the silhouette differences are quite subtle in spatial domain. Inspired by the observation that human can distinguish gaits of different subjects by adaptively focusing on temporal clips with different time scales, we propose a context-sensitive temporal feature learning (CSTL) network for gait recognition. CSTL produces temporal features in three scales, and adaptively aggregates them according to the contextual information from local and global perspectives. Specifically, CSTL contains an adaptive temporal aggregation module that subsequently performs local relation modeling and global relation modeling to fuse the multi-scale features. Besides, in order to remedy the spatial feature corruption caused by temporal operations, CSTL incorporates a salient spatial feature learning (SSFL) module to select groups of discriminative spatial features. Particularly, we utilize transformers to implement the global relation modeling and the SSFL module. To the best of our knowledge, this is the first work that adopts transformer in gait recognition. Extensive experiments conducted on three datasets demonstrate the state-of-the-art performance. Concretely, we achieve rank-1 accuracies of 98.7%, 96.2% and 88.7% under normal-walking, bag-carrying and coat-wearing conditions on CASIA-B, 97.5% on OU-MVLP and 50.6% on GREW.

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Improved gait recognition through gait energy image partitioning

Cited by 11 publications

References 20 publications

Accurate metaheuristic deep convolutional structure for a robust human gait recognition

Accurate metaheuristic deep convolutional structure for a robust human gait recognition

Dynamic Gait Recognition of Chinese Dance Based on Contour Features

Context-Sensitive Temporal Feature Learning for Gait Recognition

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