research-article

Acoustic Gait-based Person Identification using Hidden Markov Models

Authors:

Jürgen T. Geiger,

Maximilian Kneißl,

Björn W. Schuller,

Gerhard RigollAuthors Info & Claims

MAPTRAITS '14: Proceedings of the 2014 Workshop on Mapping Personality Traits Challenge and Workshop

Pages 25 - 30

https://doi.org/10.1145/2668024.2668027

Published: 12 November 2014 Publication History

Abstract

We present a system for identifying humans by their walking sounds. This problem is also known as acoustic gait recognition. The goal of the system is to analyse sounds emitted by walking persons (mostly the step sounds) and identify those persons. These sounds are characterised by the gait pattern and are influenced by the movements of the arms and legs, but also depend on the type of shoe. We extract cepstral features from the recorded audio signals and use hidden Markov models for dynamic classification. A cyclic model topology is employed to represent individual gait cycles. This topology allows to model and detect individual steps, leading to very promising identification rates. For experimental validation, we use the publicly available TUM GAID database, which is a large gait recognition database containing 3 050 recordings of 305 subjects in three variations. In the best setup, an identification rate of 65.5% is achieved out of 155 subjects. This is a relative improvement of almost 30% compared to our previous work, which used various audio features and support vector machines.

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Cited By

Laffi LRaimondi TFerrante CPagliara EBertuglia ABriefer EGamba MRavignani A(2024)The rhythm of horse gaitsAnnals of the New York Academy of Sciences10.1111/nyas.15271Online publication date: 28-Dec-2024
https://doi.org/10.1111/nyas.15271
Qu YShi LWang XWang Z(2024)Indoor Multiperson Detection and Recognition Through Footsteps: A Deep Learning Approach With Acoustic Signal AnalysisIEEE Sensors Journal10.1109/JSEN.2024.339421224:12(19482-19496)Online publication date: 15-Jun-2024
https://doi.org/10.1109/JSEN.2024.3394212
Cunningham SMcGregor I(2024)Audience perceptions of Foley footsteps and 3D realism designed to convey walker characteristicsPersonal and Ubiquitous Computing10.1007/s00779-024-01819-328:5(779-799)Online publication date: 11-Jun-2024
https://doi.org/10.1007/s00779-024-01819-3
Show More Cited By

Index Terms

Acoustic Gait-based Person Identification using Hidden Markov Models
1. Information systems
  1. Information systems applications

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Published In

cover image ACM Conferences

MAPTRAITS '14: Proceedings of the 2014 Workshop on Mapping Personality Traits Challenge and Workshop

November 2014

38 pages

ISBN:9781450339568

DOI:10.1145/2668024

Program Chairs:
Hatice Gunes
Queen Mary University of London, UK
,
Björn Schuller
Technische Universität München, Germany / Imperial College London, UK
,
Oya Celiktutan
Queen Mary University of London, UK
,
Evangelos Sariyanidi
Queen Mary University of London, UK
,
Florian Eyben
Technische Universität München, Germany

Copyright © 2014 ACM.

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New York, NY, United States

Publication History

Published: 12 November 2014

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ICMI '14

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SIGCHI

ICMI '14: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION

November 12, 2014

Istanbul, Turkey

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Cited By

Laffi LRaimondi TFerrante CPagliara EBertuglia ABriefer EGamba MRavignani A(2024)The rhythm of horse gaitsAnnals of the New York Academy of Sciences10.1111/nyas.15271Online publication date: 28-Dec-2024
https://doi.org/10.1111/nyas.15271
Qu YShi LWang XWang Z(2024)Indoor Multiperson Detection and Recognition Through Footsteps: A Deep Learning Approach With Acoustic Signal AnalysisIEEE Sensors Journal10.1109/JSEN.2024.339421224:12(19482-19496)Online publication date: 15-Jun-2024
https://doi.org/10.1109/JSEN.2024.3394212
Cunningham SMcGregor I(2024)Audience perceptions of Foley footsteps and 3D realism designed to convey walker characteristicsPersonal and Ubiquitous Computing10.1007/s00779-024-01819-328:5(779-799)Online publication date: 11-Jun-2024
https://doi.org/10.1007/s00779-024-01819-3
Kumar RIsik CMohan CPintor MChen XTramèr F(2023)Dictionary Attack on IMU-based Gait AuthenticationProceedings of the 16th ACM Workshop on Artificial Intelligence and Security10.1145/3605764.3623909(115-126)Online publication date: 30-Nov-2023
https://dl.acm.org/doi/10.1145/3605764.3623909
Cai CJin RNie JKang JZhang YLuo J(2023)PURE: Passive Multi-Person Identification via Footstep for Mobile Service NetworksIEEE Transactions on Vehicular Technology10.1109/TVT.2023.326884172:9(12221-12233)Online publication date: Sep-2023
https://doi.org/10.1109/TVT.2023.3268841
Wu SZhai XHu ZSun YLiu J(2023)Advanced acoustic footstep-based person identification dataset and method using multimodal feature fusionKnowledge-Based Systems10.1016/j.knosys.2023.110331264:COnline publication date: 15-Mar-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.110331
Fagert JMirshekari MZhang PNoh H(2022)Recursive Sparse Representation for Identifying Multiple Concurrent Occupants Using Floor Vibration SensingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35172296:1(1-33)Online publication date: 29-Mar-2022
https://dl.acm.org/doi/10.1145/3517229
Arora P(2022)Human Recognition Through Gait Audio2022 8th International Conference on Signal Processing and Communication (ICSC)10.1109/ICSC56524.2022.10009082(394-396)Online publication date: 1-Dec-2022
https://doi.org/10.1109/ICSC56524.2022.10009082
Drira SSmith I(2022)A framework for occupancy detection and tracking using floor-vibration signalsMechanical Systems and Signal Processing10.1016/j.ymssp.2021.108472168(108472)Online publication date: Apr-2022
https://doi.org/10.1016/j.ymssp.2021.108472
Topham LKhan WAl-Jumeily DWaraich AHussain A(2022)Gait Identification Using Hip Joint Movement and Deep Machine LearningIntelligent Computing Methodologies10.1007/978-3-031-13832-4_19(220-233)Online publication date: 7-Aug-2022
https://dl.acm.org/doi/10.1007/978-3-031-13832-4_19
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