The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multi-view Method for Gait Recognition Using Static Body Parameters
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Stride and Cadence as a Biometric in Automatic Person Identification and Verification
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Silhouette-Based Human Identification from Body Shape and Gait
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Individual Recognition Using Gait Energy Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved Gait Recognition by Gait Dynamics Normalization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining multiple evidences for gait recognition
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Feature Fusion of Face and Gait for Human Recognition at a Distance in Video
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Fusion of Chaotic Measure Into a New Hybrid Face-Gait System for Human Recognition
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Feature fusion of side face and gait for video-based human identification
Pattern Recognition
Multimodal biometrics using geometry preserving projections
Pattern Recognition
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Acoustic Doppler sonar for gait recogination
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Opensmile: the munich versatile and fast open-source audio feature extractor
Proceedings of the international conference on Multimedia
AVEC 2011-the first international audio/visual emotion challenge
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
Identification of humans using gait
IEEE Transactions on Image Processing
Region filling and object removal by exemplar-based image inpainting
IEEE Transactions on Image Processing
Face and Human Gait Recognition Using Image-to-Class Distance
IEEE Transactions on Circuits and Systems for Video Technology
Gait-based age estimation using a whole-generation gait database
IJCB '11 Proceedings of the 2011 International Joint Conference on Biometrics
Gait energy volumes and frontal gait recognition using depth images
IJCB '11 Proceedings of the 2011 International Joint Conference on Biometrics
| Hi-index | 0.00 |
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.