Abstract
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.
Highlights
Gait recognition has always been a challenging problem
Gait recognition became a difficult problem of computer vision
Another way to classify the descriptors extracted from the net is the Nearest Neighbour (NN) classifier
Summary
Gait recognition has always been a challenging problem. It is shown in medical and physiological studies that human gait is a unique identifier. Unlike other biometrical indices such as fingerprints, face, or iris recognition, it has a very important advantage: it can be measured at a large distance without any interaction with the human. This feature makes gait recognition applicable to intelligent video surveillance problems used, for example, in the security field. There are many factors affecting gait performance such as viewpoints, various carrying conditions, clothing, physical and medical conditions, etc. Due to these factors, gait recognition became a difficult problem of computer vision
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