Name
Abstract | ||
|---|---|---|
Gait exhibits several advantages with respect to other biometrics features: acquisition can be performed through cheap technology, at a distance and without people collaboration. In this paper we perform gait analysis using skeletal data provided by the Microsoft Kinect sensor. We defined a rich set of physical and behavioral features aiming at identifying the more relevant parameters for gait description. Using SVM we showed that a limited set of behavioral features related to the movements of head, elbows and knees is a very effective tool for gait characterization and people recognition. In particular, our experimental results shows that it is possible to achieve 96% classification accuracy when discriminating a group of 20 people. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-13386-7_2 | BIOMETRIC AUTHENTICATION (BIOMET 2014) |
Keywords | Field | DocType |
Gait characterization,Gait analysis,Kinect,Support Vector Machine | Human taxonomy,Gait,Pattern recognition,Biochemistry,Support vector machine,Chemistry,Gait analysis,Artificial intelligence,Biometrics | Conference |
Volume | ISSN | Citations |
8897 | 0302-9743 | 9 |
PageRank | References | Authors |
0.95 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Elena Gianaria | 1 | 21 | 2.21 |
| Marco Grangetto | 2 | 456 | 42.27 |
| M. Lucenteforte | 3 | 87 | 12.89 |
| Nello Balossino | 4 | 21 | 2.83 |