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.
Publisher Copyright:
© 2020 IEEE.
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.
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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 -