Learning effective Gait features using LSTM

Human gait is an important biometric feature for person identification in surveillance videos because it can be collected at a distance without subject cooperation. Most existing gait recognition methods are based on Gait Energy Image (GEI). Although the spatial information in one gait sequence can...

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Bibliographic Details
Published in2016 23rd International Conference on Pattern Recognition (ICPR) pp. 325 - 330
Main Authors Yang Feng, Yuncheng Li, Jiebo Luo
Format Conference Proceeding
LanguageEnglish
Japanese
Published IEEE 01.12.2016
Subjects
Cameras
Feature extraction
Gait recognition
Heating systems
Pose estimation
Training
Transforms
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Summary:Human gait is an important biometric feature for person identification in surveillance videos because it can be collected at a distance without subject cooperation. Most existing gait recognition methods are based on Gait Energy Image (GEI). Although the spatial information in one gait sequence can be well represented by GEI, the temporal information is lost. To solve this problem, we propose a new feature learning method for gait recognition. Not only can the learned feature preserve temporal information in a gait sequence, but it can also be applied to cross-view gait recognition. Heatmaps extracted by a convolutional neutral network (CNN) based pose estimate method are used to describe the gait information in one frame. To model a gait sequence, the LSTM recurrent neural network is naturally adopted. Our LSTM model can be trained with unlabeled data, where the identity of the subject in a gait sequence is unknown. When labeled data are available, our LSTM works as a frame to frame view transformation model (VTM). Experiments on a gait benchmark demonstrate the efficacy of our method.
DOI:10.1109/ICPR.2016.7899654