Human identification based on a kinematical data of a gait

Adam Świtoński; Romualda Mucha; Dariusz Danowski; Monika Mucha; Grzegorz Cieślar; Konrad Wojciechowski; Aleksander Sieroń

Human identification based on a kinematical data of a gait

Przetłumaczony tytuł wkładu: Identyfikacja osobnicza na podstawie kinematycznych danych chodu

Adam Świtoński
, Romualda Mucha
, Dariusz Danowski
, Monika Mucha
, Grzegorz Cieślar
, Konrad Wojciechowski
, Aleksander Sieroń

Katedra Grafiki, Wizji Komputerowej i Systemów Cyfrowych (RAU/RAU6)

Polish-Japanese Academy of Information Technology
Medical University of Silesia in Katowice
Goczalkowice Zdrój

Wyniki badań: Wkład do czasopisma › Artykuł › recenzja

6 Cytowania z bazy Scopus

Abstrakt

The paper is devoted to the gait identification challenges. It evaluates human abilities to recognize gait on the basis of skeleton animations. Further, it proposes the method of gait identification based on the kinematical data.The feature extraction approach and supervised learning are applied. To explore the most individual joints movements, aggregated feature rankings are calculated. To examine the proposed method, the database containing 353 gaits of 25 different actors is collected in the motion capture laboratory. We have obtained 99.7% of classification accuracy.

Przetłumaczony tytuł wkładu	Identyfikacja osobnicza na podstawie kinematycznych danych chodu
Język oryginału	angielski
Strony (od–do)	169-172
Liczba stron	4
Czasopismo	Przeglad Elektrotechniczny
Tom	87
Numer wydania	12 B
Status publikacji	Opublikowano - 2011

Obszary tematyczne ASJC Scopus

Inżynieria elektryczna i elektroniczna

Inne pliki i linki

Link do publikacji w Scopus

Cytowanie

@article{4a715b03daa54626896a5bb4c6abc186,

title = "Human identification based on a kinematical data of a gait",

abstract = "The paper is devoted to the gait identification challenges. It evaluates human abilities to recognize gait on the basis of skeleton animations. Further, it proposes the method of gait identification based on the kinematical data.The feature extraction approach and supervised learning are applied. To explore the most individual joints movements, aggregated feature rankings are calculated. To examine the proposed method, the database containing 353 gaits of 25 different actors is collected in the motion capture laboratory. We have obtained 99.7\% of classification accuracy.",

keywords = "Biometrics, Feature extraction, Feature selection, Gait identification, Motion capture, Supervised learning",

author = "Adam {\'S}wito{\'n}ski and Romualda Mucha and Dariusz Danowski and Monika Mucha and Grzegorz Cie{\'s}lar and Konrad Wojciechowski and Aleksander Siero{\'n}",

year = "2011",

language = "English",

volume = "87",

pages = "169--172",

journal = "Przeglad Elektrotechniczny",

issn = "0033-2097",

publisher = "Wydawnictwo SIGMA-NOT",

number = "12 B",

}

TY - JOUR

T1 - Human identification based on a kinematical data of a gait

AU - Świtoński, Adam

AU - Mucha, Romualda

AU - Danowski, Dariusz

AU - Mucha, Monika

AU - Cieślar, Grzegorz

AU - Wojciechowski, Konrad

AU - Sieroń, Aleksander

PY - 2011

Y1 - 2011

N2 - The paper is devoted to the gait identification challenges. It evaluates human abilities to recognize gait on the basis of skeleton animations. Further, it proposes the method of gait identification based on the kinematical data.The feature extraction approach and supervised learning are applied. To explore the most individual joints movements, aggregated feature rankings are calculated. To examine the proposed method, the database containing 353 gaits of 25 different actors is collected in the motion capture laboratory. We have obtained 99.7% of classification accuracy.

AB - The paper is devoted to the gait identification challenges. It evaluates human abilities to recognize gait on the basis of skeleton animations. Further, it proposes the method of gait identification based on the kinematical data.The feature extraction approach and supervised learning are applied. To explore the most individual joints movements, aggregated feature rankings are calculated. To examine the proposed method, the database containing 353 gaits of 25 different actors is collected in the motion capture laboratory. We have obtained 99.7% of classification accuracy.

KW - Biometrics

KW - Feature extraction

KW - Feature selection

KW - Gait identification

KW - Motion capture

KW - Supervised learning

UR - https://www.scopus.com/pages/publications/82655189511

M3 - Article

AN - SCOPUS:82655189511

SN - 0033-2097

VL - 87

SP - 169

EP - 172

JO - Przeglad Elektrotechniczny

JF - Przeglad Elektrotechniczny

IS - 12 B

ER -

Human identification based on a kinematical data of a gait

Abstrakt

Obszary tematyczne ASJC Scopus

Inne pliki i linki

Fingerprint

Cytowanie