PT Unknown
AU Ricard Borras
   Agata Lapedriza
   Laura Igual
TI Depth Information in Human Gait Analysis: An Experimental Study on Gender Recognition
BT 9th International Conference on Image Analysis and Recognition
PY 2012
BP 98
EP 105
VL 7325
IS II
DI 10.1007/978-3-642-31298-4_12
AB This work presents DGait, a new gait database acquired with a depth camera. This database contains videos from 53 subjects walking in different directions. The intent of this database is to provide a public set to explore whether the depth can be used as an additional information source for gait classification purposes. Each video is labelled according to subject, gender and age. Furthermore, for each subject and view point, we provide initial and final frames of an entire walk cycle. On the other hand, we perform gait-based gender classification experiments with DGait database, in order to illustrate the usefulness of depth information for this purpose. In our experiments, we extract 2D and 3D gait features based on shape descriptors, and compare the performance of these features for gender identification, using a Kernel SVM. The obtained results show that depth can be an information source of great relevance for gait classification problems.
ER