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OS-LFFD: a light and fast face detector with Ommateum structure

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Abstract

Face detection has been deployed on edge devices as the basis for face applications, but the devices cannot store large-scale models and have low computing power. The existing anchor-based face detection schemes cannot cover face images over a continuous size range, and their performance is not satisfactory. Obviously, good performances are accompanied by increased storage and lower speed. We find that the feature points in different layers correspond to a specific size range of RFs (receptive fields). According to the survey, the predictable range of RFs with the same size is the face on a continuous scale. Therefore, we argue that RFs are inherent anchors. A Light and Fast Face Detector with an Ommateum Structure (OS-LFFD) is proposed in this paper. By analyzing the correlation between the effective receptive field (ERF) and face sizes, a 4-branch network is designed to cover the objective range of face sizes. Each branch involves an ommateum block with a similar structure and shared parameters. It reduces the number of model parameters (8 M), which makes it much smaller than most face detectors. Experiments on the popular benchmarks WIDER FACE and FDDB using multiple hardware platforms demonstrate that the proposed scheme can considerably balance the accuracy and running speed.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (61976010,61702022,61802011), Beijing Municipal Education Committee Science Foundation KM201910005024, China Postdoctoral Science Foundation Funded Project (2018M640033), Beijing Excellent Young Talent Cultivation Project (2017000020124G075), and Beijing University of Technology “Ri xin” Cultivation Project.

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Dezhong Xu, Lifang Wu, Yonghao He, Qing Zhao, Meng Jian & Liang Zhao

  2. The Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Yonghao He

  3. The Department of Computer Science & Engineering, Artificial Intelligence Institute, Shanghai Jiao Tong University, Shanghai, 200240, China

    Junchi Yan

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  1. Dezhong Xu
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  2. Lifang Wu
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  3. Yonghao He
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  4. Qing Zhao
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  5. Meng Jian
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  6. Junchi Yan
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  7. Liang Zhao
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Correspondence to Qing Zhao.

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Xu, D., Wu, L., He, Y. et al. OS-LFFD: a light and fast face detector with Ommateum structure. Multimed Tools Appl 80, 34153–34172 (2021). https://doi.org/10.1007/s11042-020-09143-7

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  • DOI: https://doi.org/10.1007/s11042-020-09143-7

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