Skeleton based gait recognition for long and baggy clothes

Alharbi, Abrar; Alharbi, Fahad Radhi; Kamioka, Eiji

doi:10.1051/matecconf/201927703005

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…The KNN classifier produced results with a reasonable accuracy of 96%. A gait-based recognition system for identifying individuals in baggy and lengthy clothing was proposed in [ 17 ]. Using nine joints the system used SVM and claimed a 97% accurate result.…”

Section: Related Workmentioning

confidence: 99%

“…The majority of gender classification studies make use of a different number of biometric features for gender classification, e.g., Pries et al [ 16 ] used 13 features while Alharbi et al [ 17 ] used 7 different joints. In the literature, few joints are often used to achieve gait-based gender recognition; therefore, little focus has been given to studying the gender differences based on all joints while walking.…”

Section: Introductionmentioning

confidence: 99%

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A Gait-Based Real-Time Gender Classification System Using Whole Body Joints

Azhar

Ullah

et al. 2022

Sensors

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Gait-based gender classification is a challenging task since people may walk in different directions with varying speed, gait style, and occluded joints. The majority of research studies in the literature focused on gender-specific joints, while there is less attention on the comparison of all of a body’s joints. To consider all of the joints, it is essential to determine a person’s gender based on their gait using a Kinect sensor. This paper proposes a logistic-regression-based machine learning model using whole body joints for gender classification. The proposed method consists of different phases including gait feature extraction based on three dimensional (3D) positions, feature selection, and classification of human gender. The Kinect sensor is used to extract 3D features of different joints. Different statistical tools such as Cronbach’s alpha, correlation, t-test, and ANOVA techniques are exploited to select significant joints. The Coronbach’s alpha technique yields an average result of 99.74%, which indicates the reliability of joints. Similarly, the correlation results indicate that there is significant difference between male and female joints during gait. t-test and ANOVA approaches demonstrate that all twenty joints are statistically significant for gender classification, because the p-value for each joint is zero and less than 1%. Finally, classification is performed based on the selected features using binary logistic regression model. A total of hundred (100) volunteers participated in the experiments in real scenario. The suggested method successfully classifies gender based on 3D features recorded in real-time using machine learning classifier with an accuracy of 98.0% using all body joints. The proposed method outperformed the existing systems which mostly rely on digital images.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Gait-Based Real-Time Gender Classification System Using Whole Body Joints

Azhar

Ullah

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…In 2019, the approaches in [35, 47, 48] for skeleton‐based gait recognition were introduced. Individual skeletons with 25 joints are recorded using Microsoft Kinect, where joints’ positions are used as the gait feature [47]. Four classifiers including SVM, J48 decision tree algorithm, k‐nearest neighbour, and multilayer perceptron are tested.…”

Section: Gait Recognition Approachesmentioning

confidence: 99%