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A one-shot face detection and recognition using deep learning method for access control system

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Abstract

In this paper, we propose a face detection and recognition system using deep learning method. It can be used as an access control system that performs face detection and recognition in real-time processing. Our goal is to achieve a one-shot recognition instead of traditional two-step methods. We use SSD as the main model for face detection and VGG-Face as the main model for face recognition. We perform the deep learning method through the collection of datasets. Moreover, we use some techniques, such as data augmentation, preprocessing of the image, and post-processing of the image to train the robust face detection and recognition subsystems. We use continuous frames as input to avoid false-positive cases and make the system output without wrong results. A real demonstration system is constructed to determine the identification of the laboratory members. We use 1280 × 960 resolution video for experimental testing and achieve about 30 fps speed under GPU acceleration.

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Authors and Affiliations

  1. Department of Electrical Engineering, National Central University, No.300, Jung-Da Rd., Zhongli, 320, Taiwan, ROC

    Tsung-Han Tsai, Chi-En Tsai & Po-Ting Chi

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  1. Tsung-Han Tsai
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  2. Chi-En Tsai
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Contributions

CET conceived and designed the study. PTC performed the experiments. THT reviewed and edited the manuscript. All authors read and approved the manuscript.

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Correspondence to Tsung-Han Tsai.

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Tsai, TH., Tsai, CE. & Chi, PT. A one-shot face detection and recognition using deep learning method for access control system. SIViP 17, 1571–1579 (2023). https://doi.org/10.1007/s11760-022-02366-1

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