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Anthropometric and human gait identification using skeleton data from Kinect sensor

Published: 24 March 2014 Publication History

Abstract

This work investigates the use of 3D skeleton data retrieved from Microsoft Kinect sensor to obtain gait kinematic parameters from human walk for biometric identification. Several subjects were captured walking in front of the Kinect sensor resulting in a data set containing several 3D points from skeleton joints of each person. These points were used to extract anthropometric information and calculate angles described by lower joints (hips, knees and ankles) during walk in order to extract gait kinematic parameters. Statistical descriptors were used in the resulting curves to define attributes to train machine learning models (K-Nearest Neighbor and Multilayer Perceptron) and the models tested for its efficacy in identifying individuals from the attributes. The results were validated using the 10-fold cross-validation method and showed an overall high rate of accuracy.

References

[1]
R. Araújo, G. Graña and V. Andersson, "Towards skeleton biometric identification using the Microsoft Kinect Sensor," ACM Symposium on applied computing 2013.
[2]
J. H. Yoo and M. S. Nixon, "Automated markless analysis of human gait motion for recognition and classification," in ETRI, vol. 33, no. 2, pp. 259--266, 2011.
[3]
D. Cunado, M. S. Nixon and J. N. Carter "Automatic extraction and description of human gait models for recognition purposes," Elsevier Computer Vision and Image Understanding, vol. 90, no. 1, pp 1--44, 2003.
[4]
M. Hofmann, S. Bachman and G. Rigoll,"2.5D Gait Biometrics using the Depth Gradient Histogram Energy Image," BTAS IEEE Fifth International Conference pp. 399--403, 2012.
[5]
B. C. Munsell et. al, "Person identification using full-body motion and anthropometric biometrics from kinect videos," ECCV 2012. pp. 91--100, 2012.

Cited By

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  • (2022)A Survey of Human Gait-Based Artificial Intelligence ApplicationsFrontiers in Robotics and AI10.3389/frobt.2021.7492748Online publication date: 3-Jan-2022
  • (2022)Human Body Pose Estimation for Gait Identification: A Comprehensive Survey of Datasets and ModelsACM Computing Surveys10.1145/353338455:6(1-42)Online publication date: 7-Dec-2022
  • (2020)Ensemble Learning for Skeleton-Based Body Mass Index ClassificationApplied Sciences10.3390/app1021781210:21(7812)Online publication date: 4-Nov-2020
  • Show More Cited By

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cover image ACM Conferences
SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied Computing
March 2014
1890 pages
ISBN:9781450324694
DOI:10.1145/2554850
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 March 2014

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Author Tags

  1. anthropometry
  2. biometrics
  3. gait
  4. machine learning

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  • Research-article

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SAC 2014
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SAC 2014: Symposium on Applied Computing
March 24 - 28, 2014
Gyeongju, Republic of Korea

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SAC '14 Paper Acceptance Rate 218 of 939 submissions, 23%;
Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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The 40th ACM/SIGAPP Symposium on Applied Computing
March 31 - April 4, 2025
Catania , Italy

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Cited By

View all
  • (2022)A Survey of Human Gait-Based Artificial Intelligence ApplicationsFrontiers in Robotics and AI10.3389/frobt.2021.7492748Online publication date: 3-Jan-2022
  • (2022)Human Body Pose Estimation for Gait Identification: A Comprehensive Survey of Datasets and ModelsACM Computing Surveys10.1145/353338455:6(1-42)Online publication date: 7-Dec-2022
  • (2020)Ensemble Learning for Skeleton-Based Body Mass Index ClassificationApplied Sciences10.3390/app1021781210:21(7812)Online publication date: 4-Nov-2020
  • (2020)Human Skeleton Data Augmentation for Person Identification over Deep Neural NetworkApplied Sciences10.3390/app1014484910:14(4849)Online publication date: 15-Jul-2020
  • (2020)Fruit Morphological Measurement Based on Three-Dimensional ReconstructionAgronomy10.3390/agronomy1004045510:4(455)Online publication date: 25-Mar-2020
  • (2020)Human gait recognition by shoulder movement's Doppler signature using SVM classifierINTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 201910.1063/5.0027556(040005)Online publication date: 2020
  • (2019)Gait Recognition from Markerless 3D Motion Capture2019 International Conference on Biometrics (ICB)10.1109/ICB45273.2019.8987318(1-6)Online publication date: Jun-2019
  • (2019)Review on Current Methods of Gait Analysis and Recognition using Kinect2019 IEEE 15th International Colloquium on Signal Processing & Its Applications (CSPA)10.1109/CSPA.2019.8695979(229-234)Online publication date: Mar-2019
  • (2019)Skeleton based gait recognition for long and baggy clothesMATEC Web of Conferences10.1051/matecconf/201927703005277(03005)Online publication date: 2-Apr-2019
  • (2018)Riemannian Spatio-Temporal Features of Locomotion for Individual RecognitionSensors10.3390/s1901005619:1(56)Online publication date: 23-Dec-2018
  • Show More Cited By

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