Personal Identification Methods Using Footsteps of One Step

Yuki Hori; Takahiro Ando; Akira Fukuda

doi:10.1109/ICAIIC48513.2020.9065230

Personal Identification Methods Using Footsteps of One Step

Yuki Hori, Takahiro Ando, Akira Fukuda

Advanced Software Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In recent years, it has been shown that it is possible to identify individuals by walking footsteps, and research is being conducted to improve the recognition rate. The conventional method targets multiple footsteps and imposes many restrictions on the recording conditions. The task has further required precise, manual segmentation of the sequence of footsteps for analysis of the observation waveform. On the other hand, in our previous research, we have proposed a method that can robustly identify only the footstep of one step, with few restrictions for recording conditions and extraction methods. In this paper, we investigate whether our proposed method can identify individuals only by the difference in walking sound for each person regardless of the presence and difference of shoes. As a result, we found that the proposal method using SVM or CNN can identify with average accuracy 98 % and distinguish between subjects without being affected by shoes.

Original language	English
Title of host publication	2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	73-78
Number of pages	6
ISBN (Electronic)	9781728149851
DOIs	https://doi.org/10.1109/ICAIIC48513.2020.9065230
Publication status	Published - Feb 2020
Event	2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020 - Fukuoka, Japan Duration: Feb 19 2020 → Feb 21 2020

Publication series

Name	2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020

Conference

Conference	2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020
Country	Japan
City	Fukuoka
Period	2/19/20 → 2/21/20

All Science Journal Classification (ASJC) codes

Information Systems and Management
Artificial Intelligence
Computer Networks and Communications
Computer Vision and Pattern Recognition
Information Systems
Signal Processing

Access to Document

10.1109/ICAIIC48513.2020.9065230

Fingerprint Dive into the research topics of 'Personal Identification Methods Using Footsteps of One Step'. Together they form a unique fingerprint.

Cite this

Hori, Y., Ando, T., & Fukuda, A. (2020). Personal Identification Methods Using Footsteps of One Step. In 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020 (pp. 73-78). [9065230] (2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICAIIC48513.2020.9065230

Personal Identification Methods Using Footsteps of One Step. / Hori, Yuki; Ando, Takahiro; Fukuda, Akira.

2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 73-78 9065230 (2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Hori, Y, Ando, T & Fukuda, A 2020, Personal Identification Methods Using Footsteps of One Step. in 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020., 9065230, 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Institute of Electrical and Electronics Engineers Inc., pp. 73-78, 2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, Fukuoka, Japan, 2/19/20. https://doi.org/10.1109/ICAIIC48513.2020.9065230

Hori Y, Ando T, Fukuda A. Personal Identification Methods Using Footsteps of One Step. In 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 73-78. 9065230. (2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020). https://doi.org/10.1109/ICAIIC48513.2020.9065230

Hori, Yuki ; Ando, Takahiro ; Fukuda, Akira. / Personal Identification Methods Using Footsteps of One Step. 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 73-78 (2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020).

@inproceedings{31d0d094b5a04cb5871f78c935159c96,

title = "Personal Identification Methods Using Footsteps of One Step",

abstract = "In recent years, it has been shown that it is possible to identify individuals by walking footsteps, and research is being conducted to improve the recognition rate. The conventional method targets multiple footsteps and imposes many restrictions on the recording conditions. The task has further required precise, manual segmentation of the sequence of footsteps for analysis of the observation waveform. On the other hand, in our previous research, we have proposed a method that can robustly identify only the footstep of one step, with few restrictions for recording conditions and extraction methods. In this paper, we investigate whether our proposed method can identify individuals only by the difference in walking sound for each person regardless of the presence and difference of shoes. As a result, we found that the proposal method using SVM or CNN can identify with average accuracy 98 % and distinguish between subjects without being affected by shoes.",

author = "Yuki Hori and Takahiro Ando and Akira Fukuda",

note = "Funding Information: ACKNOWLEDGMENT This work is partially supported by JSPS KAKENHI Grant Number JP15H05708.; 2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020 ; Conference date: 19-02-2020 Through 21-02-2020",

year = "2020",

month = feb,

doi = "10.1109/ICAIIC48513.2020.9065230",

language = "English",

series = "2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "73--78",

booktitle = "2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020",

address = "United States",

}

TY - GEN

T1 - Personal Identification Methods Using Footsteps of One Step

AU - Hori, Yuki

AU - Ando, Takahiro

AU - Fukuda, Akira

N1 - Funding Information: ACKNOWLEDGMENT This work is partially supported by JSPS KAKENHI Grant Number JP15H05708.

PY - 2020/2

Y1 - 2020/2

N2 - In recent years, it has been shown that it is possible to identify individuals by walking footsteps, and research is being conducted to improve the recognition rate. The conventional method targets multiple footsteps and imposes many restrictions on the recording conditions. The task has further required precise, manual segmentation of the sequence of footsteps for analysis of the observation waveform. On the other hand, in our previous research, we have proposed a method that can robustly identify only the footstep of one step, with few restrictions for recording conditions and extraction methods. In this paper, we investigate whether our proposed method can identify individuals only by the difference in walking sound for each person regardless of the presence and difference of shoes. As a result, we found that the proposal method using SVM or CNN can identify with average accuracy 98 % and distinguish between subjects without being affected by shoes.

AB - In recent years, it has been shown that it is possible to identify individuals by walking footsteps, and research is being conducted to improve the recognition rate. The conventional method targets multiple footsteps and imposes many restrictions on the recording conditions. The task has further required precise, manual segmentation of the sequence of footsteps for analysis of the observation waveform. On the other hand, in our previous research, we have proposed a method that can robustly identify only the footstep of one step, with few restrictions for recording conditions and extraction methods. In this paper, we investigate whether our proposed method can identify individuals only by the difference in walking sound for each person regardless of the presence and difference of shoes. As a result, we found that the proposal method using SVM or CNN can identify with average accuracy 98 % and distinguish between subjects without being affected by shoes.

UR - http://www.scopus.com/inward/record.url?scp=85084047465&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85084047465&partnerID=8YFLogxK

U2 - 10.1109/ICAIIC48513.2020.9065230

DO - 10.1109/ICAIIC48513.2020.9065230

M3 - Conference contribution

AN - SCOPUS:85084047465

T3 - 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020

SP - 73

EP - 78

BT - 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020

Y2 - 19 February 2020 through 21 February 2020

ER -