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A Real-Time and Long-Term Face Tracking Method Using Convolutional Neural Network and Optical Flow in IoT-Based Multimedia Communication Systems
Wireless Communications and Mobile Computing, Volume: 2021, Pages: 1 - 15
Swansea University Authors: Hans Ren, Freya Hu, Chi Zhang
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© 2021 Hanchi Ren et al. This is an open access article distributed under the Creative Commons Attribution License
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DOI (Published version): 10.1155/2021/6711561
Abstract
The development of the Internet of Things (IoT) stimulates many research works related to Multimedia Communication Systems (MCS), such as human face detection and tracking. This trend drives numerous progressive methods. Among these methods, the deep learning-based methods can spot face patch in an...
| Published in: | Wireless Communications and Mobile Computing |
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| ISSN: | 1530-8669 1530-8677 |
| Published: |
Hindawi Limited
2021
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa58621 |
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| Abstract: |
The development of the Internet of Things (IoT) stimulates many research works related to Multimedia Communication Systems (MCS), such as human face detection and tracking. This trend drives numerous progressive methods. Among these methods, the deep learning-based methods can spot face patch in an image effectively and accurately. Many people consider face tracking as face detection, but they are two different techniques. Face detection focuses on a single image, whose shortcoming is obvious, such as unstable and unsmooth face position when adopted on a sequence of continuous images; computing is expensive due to its heavy reliance on Convolutional Neural Networks (CNNs) and limited detection performance on the edge device. To overcome these defects, this paper proposes a novel face tracking strategy by combining CNN and optical flow, namely, C-OF, which achieves an extremely fast, stable, and long-term face tracking system. Two key things for commercial applications are the stability and smoothness of face positions in a sequence of image frames, which can provide more probability for face biological signal extraction, silent face antispoofing, and facial expression analysis in the fields of IoT-based MCS. Our method captures face patterns in every two consequent frames via optical flow to get rid of the unstable and unsmooth problems. Moreover, an innovative metric for measuring the stability and smoothness of face motion is designed and adopted in our experiments. The experimental results illustrate that our proposed C-OF outperforms both face detection and object tracking methods. |
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| Keywords: |
Electrical and Electronic Engineering, Computer Networks and Communications, Information Systems |
| College: |
Faculty of Science and Engineering |
| Funders: |
This study is supported by the National Key Technology R&D Program of China (No. 2019YFC1606401), Beijing Natural Science Foundation (No. 4202014), and Humanity and Social Science Youth Foundation of Ministry of Education of China (No. 20YJCZH229) |
| Start Page: |
1 |
| End Page: |
15 |