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

• Main Authors: Chao Li, Xin Min, Shouqian Sun, Wenqian Lin, Zhichuan Tang
• Format: Article
• Language: English
• Published: MDPI AG 2017-02-01
• Series: Applied Sciences
• Subjects: deep convolutional features; gait representation; Joint Bayesian; cross-view gait recognition; gait identification; gait verification
• Online Access: http://www.mdpi.com/2076-3417/7/3/210

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.