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CFSM: a novel frame analyzing mechanism for real-time face recognition system on the embedded system

Published: 01 January 2022 Publication History

Abstract

The development of web cameras and smart phones is mature, and more and more facial recognition-related applications are implemented on embedded systems. The demand for real-time face recognition on embedded systems is also increasing. In order to improve the accuracy of face recognition, most of the modern face recognition systems consist of multiple deep neural network models for recognition. However, in an embedded system, integrating these complex neural network models and execute simultaneously is not easy to achieve the goal of real-time recognition of human faces and their identities. In view of this, this study proposes a new frame analysis mechanism, continuous frames skipping mechanism (CFSM), which can analyze the frame in real time to determine whether it is necessary to perform face recognition on the current frame. Through the analysis of CFSM, the frames that do not need to be re-recognized for face are omitted. In this way, the workload of the face recognition system will be greatly reduced to achieve the goal of real-time face recognition in the embedded system. The experimental results show that the proposed CFSM mechanism can greatly increase the speed of face recognition in the video on the embedded system, achieving the goal of real-time face recognition.

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Cited By

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  • (2023)Reducing false positive rate with the help of scene change indicator in deep learning based real-time face recognition systemsMultimedia Tools and Applications10.1007/s11042-023-15769-082:30(47517-47536)Online publication date: 1-Dec-2023

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        Information & Contributors

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        Published In

        cover image Multimedia Tools and Applications
        Multimedia Tools and Applications  Volume 81, Issue 2
        Jan 2022
        1497 pages

        Publisher

        Kluwer Academic Publishers

        United States

        Publication History

        Published: 01 January 2022
        Accepted: 22 September 2021
        Revision received: 14 July 2021
        Received: 12 September 2020

        Author Tags

        1. Deep learning
        2. Face recognition
        3. Embedded system
        4. Real-time
        5. Frame analysis

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        • (2023)Reducing false positive rate with the help of scene change indicator in deep learning based real-time face recognition systemsMultimedia Tools and Applications10.1007/s11042-023-15769-082:30(47517-47536)Online publication date: 1-Dec-2023

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