ACM Transactions on Graphics (TOG)
The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Individual Recognition Using Gait Energy Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fusing gait and face cues for human gender recognition
Neurocomputing
A study on gait-based gender classification
IEEE Transactions on Image Processing
Gait analysis of gender and age using a large-scale multi-view gait database
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Real-time human pose recognition in parts from single depth images
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Gait Components and Their Application to Gender Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Morphological gender recognition by a social robot and privacy concerns: late breaking reports
Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction
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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.