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Siamese Neural Network based Gait Recognition for Human Identification

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Citation Author(s):
Cheng Zhang, Wu Liu, Huadong Ma, Huiyuan Fu
Submitted by:
Cheng Zhang
Last updated:
19 March 2016 - 8:39am
Document Type:
Poster
Document Year:
2016
Event:
Presenters Name:
Cheng Zhang

Abstract 

Abstract: 

As the remarkable characteristics of remote accessed, robust and security, gait recognition has gained significant attention in the biometrics based human identification task. However, the existed methods mainly employ the handcrafted gait features, which cannot well handle the indistinctive inter-class differences and large intra-class variations of human gait in real-world situation. In this paper, we have developed a Siamese neural network based gait recognition framework to automatically extract robust and discriminative gait features for human identification. Different from conventional deep neural network, the Siamese network can employ distance metric learning to drive the similarity metric to be small for pairs of gait from the same person, and large for pairs from different persons. In particular, to further learn effective model with limited training data, we composite the gait energy images instead of raw sequence of gaits. Consequently, the experiments on the world’s largest gait database show our framework impressively outperforms state-of-the-arts.

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Dataset Files

Poster For ICASSP 2016 - Siamese Neural Network based Gait Recognition for Human Identification.pdf

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