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.
Highlights
With the development of AI technology [1,2,3], Internet of Things (IoT) [4,5,6,7] is receiving more and more attention from academia
In order to filter the representation in the bounding box generated by the optical flow face tracker, we show a very simple face identifier that works very well in distinguishing the background
We proposed a stable, smooth, super real-time, and long-term face tracking system using lightweight Convolutional Neural Networks (CNNs) and optical flow, namely, C-OF, which consists of a face detector, face tracker, and face identifier
Summary
With the development of AI technology [1,2,3], IoT [4,5,6,7] is receiving more and more attention from academia. It emphasizes that all objects connected to the internet (including people and machines) have unique addresses and communicate through wired and wireless networks and have been deeply integrated into humans’ daily life. The smarter the humans’ life is, the more dangerous the privacy is. Every smart device is “monitoring” you, so personal data protection and privacy-preserved problems should be paid more attention to. Deng and Xie [53] proposed a nested CNN-cascade learning algorithm that adopts shallow CNN architectures. All these are face detection methods, which all focus on single image representation only. That is why on a continuous video, bounding boxes generated by them are unstable and unsmooth
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