Human Gait Recognition System

Jawed, Bilal; Khalifa, Othman O.; Bhuiyan, Sharif Shah Newaj

doi:10.1109/iccce.2018.8539245

Cited by 14 publications

(5 citation statements)

References 9 publications

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“…For any conceivable moment, type p and q, intensity image functions f(x,y) with N x M pixel dimensions can be computed, as can their characterization in the context of universal computation, as in Eq. (7).…”

Section: Invariant Moment Of Imagementioning

confidence: 99%

“…The gait recognition system is a cutting-edge technological solution that does not necessarily necessitate assuming a specific posture or positioning oneself in front of biometric equipment. Instead, it utilizes covert technology that examines human gait motions to ascertain if an individual presents a security risk or is behaving abnormally [7,8]. Both model-free and model-based approaches can be used for gait identification.…”

Section: Introductionmentioning

confidence: 99%

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Advanced Age Group Estimation Using Gait Analysis: A Novel Multi-Energy Image and Invariant Moments Method

Al Musalhi,

Celebi

2024

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The precise estimation of age is pivotal in identity verification across critical security checkpoints, including seaports, land borders, and airports. This study introduces an innovative methodology for age estimation based on gait analysis, enhancing security measures and providing reliable identity confirmation. Utilizing a novel preprocessing technique on gait datasets, this approach amalgamates three integral components: the Accumulated Frame Difference Energy Image (AFDEI), the Gait Energy Image (GEI), and the Invariant Moment of the image. These elements collectively facilitate the efficient extraction and analysis of critical gait data. Evaluation of the model, employing a Convolutional Neural Network (CNN), was conducted on the publicly available OU-ISRI with age dataset. The model demonstrated remarkable proficiency, achieving an average accuracy of 90.40% across 14 distinct view angles within a 5 K-Fold framework. This methodological advancement significantly outperforms existing state-of-the-art techniques in accuracy. The findings highlight the efficacy and potential of the proposed method for age grouping estimation through human gait analysis. Despite these advancements, it is crucial to acknowledge the study's limitations, particularly the dependency on silhouette images. Preprocessing is essential prior to implementing the proposed methodology. The outcomes of this study are instrumental in reinforcing age estimation as a key factor in bolstering identity verification processes at essential security junctures.

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Section: Invariant Moment Of Imagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Advanced Age Group Estimation Using Gait Analysis: A Novel Multi-Energy Image and Invariant Moments Method

Al Musalhi,

Celebi

2024

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“…Lam et al [68] separated background from foreground in video image sequences by filtering and extracting silhouettes. Jawed et al [69] used silhouette images to develop a human gait recognition system. In this study, silhouette images are obtained using background and frame difference, using statistical features such as variance and covariance of scene brightness to extract the background.…”

Section: State Of the Artmentioning

confidence: 99%

Deep Learning Approach for Human Action Recognition Using a Time Saliency Map Based on Motion Features Considering Camera Movement and Shot in Video Image Sequences

Alavigharahbagh,

Hajihashemi,

Machado

et al. 2023

Information

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In this article, a hierarchical method for action recognition based on temporal and spatial features is proposed. In current HAR methods, camera movement, sensor movement, sudden scene changes, and scene movement can increase motion feature errors and decrease accuracy. Another important aspect to take into account in a HAR method is the required computational cost. The proposed method provides a preprocessing step to address these challenges. As a preprocessing step, the method uses optical flow to detect camera movements and shots in input video image sequences. In the temporal processing block, the optical flow technique is combined with the absolute value of frame differences to obtain a time saliency map. The detection of shots, cancellation of camera movement, and the building of a time saliency map minimise movement detection errors. The time saliency map is then passed to the spatial processing block to segment the moving persons and/or objects in the scene. Because the search region for spatial processing is limited based on the temporal processing results, the computations in the spatial domain are drastically reduced. In the spatial processing block, the scene foreground is extracted in three steps: silhouette extraction, active contour segmentation, and colour segmentation. Key points are selected at the borders of the segmented foreground. The last used features are the intensity and angle of the optical flow of detected key points. Using key point features for action detection reduces the computational cost of the classification step and the required training time. Finally, the features are submitted to a Recurrent Neural Network (RNN) to recognise the involved action. The proposed method was tested using four well-known action datasets: KTH, Weizmann, HMDB51, and UCF101 datasets and its efficiency was evaluated. Since the proposed approach segments salient objects based on motion, edges, and colour features, it can be added as a preprocessing step to most current HAR systems to improve performance.

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“…The gait recognition system is an innovative piece of technology that does not necessarily need to be in a certain position or in standing in front of biometric devices. Instead, it relies on stealthy technology that analyzes human gait gestures to determine whether an individual poses a threat to security or is acting unusually [1], [2]. Both model-free and model-based approaches can be utilized in the process of gait identification; nevertheless, the two styles of gait analysis are the most prevalent at present.…”

Section: Introductionmentioning

confidence: 99%

Age Estimation in Human Gait Extraction using A Combination of Multi-Energy Image with Invariant Moment

Musalhi¹,

Çelebi²

2023

Preprint

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Accurately estimating a person's age is crucial for identity verification at all critical checkpoints, including airports, land borders, and seaports. Human gait may be used as a biometric identifier and indicator of age, among other things. This study aims to develop methods for estimating a person's age by observing their walk. In this paper, a novel technique for preprocessing the proposed gait dataset has been used by utilizing a combination of the gait energy image (GEI), the accumulated frame difference energy image (AFDEI), and the invariant moment of the image. The proposed technique outperformed state-of-the-art methods in terms of accuracy. The proposed method was tested and evaluated using a convolutional neural network (CNN), and it achieved an average accuracy of 90.35% across 14 different view angles within 5 K-Fold, the proposed method resulted in 94.68% and 94.54% in 30º and 75º view degree respectively. concluding that the approach is effective and promising for estimating age using human gait.

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Human Gait Recognition System

Cited by 14 publications

References 9 publications

Advanced Age Group Estimation Using Gait Analysis: A Novel Multi-Energy Image and Invariant Moments Method

Advanced Age Group Estimation Using Gait Analysis: A Novel Multi-Energy Image and Invariant Moments Method

Deep Learning Approach for Human Action Recognition Using a Time Saliency Map Based on Motion Features Considering Camera Movement and Shot in Video Image Sequences

Age Estimation in Human Gait Extraction using A Combination of Multi-Energy Image with Invariant Moment

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