TY - GEN
T1 - Utilizing motion data retrieval techniques for person identification
AU - Juang, Jing Fu
AU - Teng, Wei Guang
PY - 2016/12/30
Y1 - 2016/12/30
N2 - Identifying a specific user is an old but challenging problem, and its applications are ubiquitous in our daily lives. Conventional person identification methods are using an ID card or the combination of a username and password. Recently, new techniques based on biometrics have been introduced so that people do not need to worry if they forget their username and password. For example, fingerprint and iris recognition are becoming common methods of person identification; however, users are usually required to interact with a system to use these traits. In some non-critical situations, it may be more convenient to utilize soft biometrics for person identification, although these features are not as unique for a specific person. In this work, we propose to conduct gait analysis that can be performed from a distance without disturbing user activities. We utilize depth cameras to capture user movements and create motion sequences. Then, a motion sequence is transformed to a motion string with appropriate data preprocessing and clustering techniques. Representative motion strings representing the individual behaviour of a user are retrieved and utilized to identify people. Empirical studies based on real motion data show that our approach performs well in person identification.
AB - Identifying a specific user is an old but challenging problem, and its applications are ubiquitous in our daily lives. Conventional person identification methods are using an ID card or the combination of a username and password. Recently, new techniques based on biometrics have been introduced so that people do not need to worry if they forget their username and password. For example, fingerprint and iris recognition are becoming common methods of person identification; however, users are usually required to interact with a system to use these traits. In some non-critical situations, it may be more convenient to utilize soft biometrics for person identification, although these features are not as unique for a specific person. In this work, we propose to conduct gait analysis that can be performed from a distance without disturbing user activities. We utilize depth cameras to capture user movements and create motion sequences. Then, a motion sequence is transformed to a motion string with appropriate data preprocessing and clustering techniques. Representative motion strings representing the individual behaviour of a user are retrieved and utilized to identify people. Empirical studies based on real motion data show that our approach performs well in person identification.
UR - http://www.scopus.com/inward/record.url?scp=85011255977&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85011255977&partnerID=8YFLogxK
U2 - 10.1109/ICKEA.2016.7803016
DO - 10.1109/ICKEA.2016.7803016
M3 - Conference contribution
AN - SCOPUS:85011255977
T3 - 2016 IEEE International Conference on Knowledge Engineering and Applications, ICKEA 2016
SP - 188
EP - 192
BT - 2016 IEEE International Conference on Knowledge Engineering and Applications, ICKEA 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE International Conference on Knowledge Engineering and Applications, ICKEA 2016
Y2 - 28 September 2016 through 30 September 2016
ER -