Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10360063 | Journal of Visual Communication and Image Representation | 2014 | 12 Pages |
Abstract
Recognizing people by the way they walk-also known as gait recognition-has been studied extensively in the recent past. Recent gait recognition methods solely focus on data extracted from an RGB video stream. With this work, we provide a means for multimodal gait recognition, by introducing the freely available TUM Gait from Audio, Image and Depth (GAID) database. This database simultaneously contains RGB video, depth and audio. With 305 people in three variations, it is one of the largest to-date. To further investigate challenges of time variation, a subset of 32 people is recorded a second time. We define standardized experimental setups for both person identification and for the assessment of the soft biometrics age, gender, height, and shoe type. For all defined experiments, we present several baseline results on all available modalities. These effectively demonstrate multimodal fusion being beneficial to gait recognition.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Martin Hofmann, Jürgen Geiger, Sebastian Bachmann, Björn Schuller, Gerhard Rigoll,