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Appl. Sci. 2017, 7(3), 210; doi:10.3390/app7030210

DeepGait: A Learning Deep Convolutional Representation for View-Invariant Gait Recognition Using Joint Bayesian

1
College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
2
Industrial Design Institute, Zhejiang University of Technology, Hangzhou 310023, China
*
Author to whom correspondence should be addressed.
Academic Editor: José Antonio Iglesias Martínez
Received: 3 January 2017 / Revised: 27 January 2017 / Accepted: 15 February 2017 / Published: 23 February 2017
(This article belongs to the Special Issue Human Activity Recognition)
View Full-Text   |   Download PDF [5047 KB, uploaded 23 February 2017]   |  

Abstract

Human gait, as a soft biometric, helps to recognize people through their walking. To further improve the recognition performance, we propose a novel video sensor-based gait representation, DeepGait, using deep convolutional features and introduce Joint Bayesian to model view variance. DeepGait is generated by using a pre-trained “very deep” network “D-Net” (VGG-D) without any fine-tuning. For non-view setting, DeepGait outperforms hand-crafted representations (e.g., Gait Energy Image, Frequency-Domain Feature and Gait Flow Image, etc.). Furthermore, for cross-view setting, 256-dimensional DeepGait after PCA significantly outperforms the state-of-the-art methods on the OU-ISR large population (OULP) dataset. The OULP dataset, which includes 4007 subjects, makes our result reliable in a statistically reliable way. View Full-Text
Keywords: deep convolutional features; gait representation; Joint Bayesian; cross-view gait recognition; gait identification; gait verification deep convolutional features; gait representation; Joint Bayesian; cross-view gait recognition; gait identification; gait verification
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Li, C.; Min, X.; Sun, S.; Lin, W.; Tang, Z. DeepGait: A Learning Deep Convolutional Representation for View-Invariant Gait Recognition Using Joint Bayesian. Appl. Sci. 2017, 7, 210.

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