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Aerial face recognition and absolute distance estimation using drone and deep learning

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Abstract

Because of the rise of deep learning and neural networks, algorithms based on deep learning have also been developed and subtly applied in daily life. This paper hoped to use neural network-based face recognition with absolute distance estimation and research done with drones to achieve the target tracking effect through the recognition ability of neural networks. The aerial images were used to identify different people or behaviors through which distances could be calculated to establish the foundation for subsequent research and development. According to our experiments, performing the face recognition model and distance estimation met expected results, and it also had a specific accuracy rate from different angles and distances. In summary, identification and distance estimate had better accuracy within the effective length, and we could expect the speed and convenience of identification to be realized on other devices.

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Authors and Affiliations

  1. College of Languages, National Taichung University of Science and Technology, Taichung City, Taiwan

    Ying-Hung Pu

  2. Department of E-Learning Design and Management, National Chiayi University, Chiayi City, Taiwan

    Po-Sheng Chiu

  3. Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung City, Taiwan

    Yu-Shiuan Tsai, Meng-Tsung Liu & Shih-Syun Lin

  4. Department of Electrical Engineering, National Taiwan Ocean University, Keelung City, Taiwan

    Yi-Zeng Hsieh

  5. Center of Excellence for Ocean Engineering, National Taiwan Ocean University, Keelung City, Taiwan

    Yi-Zeng Hsieh

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  1. Ying-Hung Pu
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  2. Po-Sheng Chiu
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  3. Yu-Shiuan Tsai
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  4. Meng-Tsung Liu
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  5. Yi-Zeng Hsieh
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  6. Shih-Syun Lin
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Correspondence to Yu-Shiuan Tsai.

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Pu, YH., Chiu, PS., Tsai, YS. et al. Aerial face recognition and absolute distance estimation using drone and deep learning. J Supercomput 78, 5285–5305 (2022). https://doi.org/10.1007/s11227-021-04088-6

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  • DOI: https://doi.org/10.1007/s11227-021-04088-6

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