A Human Identity Recognition System Based on Kinect Skeletal Tracking and ANN Classifier

DOI

10.2991/aiie-16.2016.57How to use a DOI?

Keywords

human identity recognition; kinect; ANN classifier; skeletal identification

Abstract

We proposed a novel method of people recognition based on the structure of bones, which is detected by Kinect sensor for skeletal tracking. By using 3D depth image achieved by Kinect, body's skeleton information is extracted as 20 joints with joint coordinates, from which body's skeletal features are calculated into different length ratio between each bone. Later these features are processed by a BP artificial neural network (ANN) classifier to differentiate several samples. The results show a high accuracy up to 98% test samples, which indicate that the body's skeletal features can provide the effective judgment in people recognition.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xianhui Zeng
AU  - Jinwen Deng
AU  - Mujun Liu
AU  - Yue Wang
AU  - Xuejun Zhang
PY  - 2016/11
DA  - 2016/11
TI  - A Human Identity Recognition System Based on Kinect Skeletal Tracking and ANN Classifier
BT  - Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)
PB  - Atlantis Press
SP  - 244
EP  - 247
SN  - 1951-6851
UR  - https://doi.org/10.2991/aiie-16.2016.57
DO  - 10.2991/aiie-16.2016.57
ID  - Zeng2016/11
ER  -