The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-2/W4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W4, 207–212, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W4-207-2017
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W4, 207–212, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W4-207-2017

  10 May 2017

10 May 2017

GAIT RECOGNITION BASED ON CONVOLUTIONAL NEURAL NETWORKS

A. Sokolova1 and A. Konushin1,2 A. Sokolova and A. Konushin
  • 1National Research University Higher School of Economics, Moscow, Russia
  • 2Lomonosov Moscow State University, Moscow, Russia

Keywords: Gait Recognition, Biometrics, Convolutional Neural Networks, Optical Flow

Abstract. 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.