GAIT RECOGNITION BASED ON CONVOLUTIONAL NEURAL NETWORKS

In this work we investigate the problem of people recognition by their gait. For this task, we implement deep learning approach using the optical flow as the main source of motion information and combine neural feature extraction with the additional embedding of descriptors for representation improv...

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Bibliographic Details
Published inInternational archives of the photogrammetry, remote sensing and spatial information sciences. Vol. XLII-2/W4; pp. 207 - 212
Main Authors Sokolova, A., Konushin, A.
Format Journal Article Conference Proceeding
LanguageEnglish
Published Gottingen Copernicus GmbH 01.01.2017
Subjects
Artificial neural networks
Biometrics
Classification
Feature extraction
Gait
Gait recognition
Machine learning
Neural networks
Optical flow (image analysis)
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Summary:In this work we investigate the problem of people recognition by their gait. For this task, we implement deep learning approach using the optical flow as the main source of motion information and combine neural feature extraction with the additional embedding of descriptors for representation improvement. In order to find the best heuristics, we compare several deep neural network architectures, learning and classification strategies. The experiments were made on two popular datasets for gait recognition, so we investigate their advantages and disadvantages and the transferability of considered methods.
ISSN:2194-9034
1682-1750
2194-9034
DOI:10.5194/isprs-archives-XLII-2-W4-207-2017