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A Quantitative Study on the FaceNet System

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Advanced Computational and Communication Paradigms (ICACCP 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 535))

Abstract

Face recognition is the science of identifying and recognizing human faces in various situations, keeping the constraints like pose variation and occlusion in mind. Due to its impactful applications in safety and security systems, face recognition is becoming extremely popular and is researched extensively even today. The FaceNet method is among the most tested approaches in Deep learning face identification. This method uses a deep convolution neural network for training the data. The face embedding generated can be used to train a face identification system. This study aims to comprehend the FaceNet system, evaluate its performance, and test its accuracy on seven standard datasets. The study also tries to compare how well the FaceNet method works compared to other popular holistic and hybrid methods. From the outcomes of this study, we can conclude that FaceNet showed outstanding results and was better than the other methods. The FaceNet system reached a minimum of 90% accuracy on all standard datasets used on both the pre-trained models, which is a significant number for any face recognition method.

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Acknowledgements

The authors profoundly acknowledge the Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, INDIA-576104, for providing a supportive environment to make this work conceivable.

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Authors and Affiliations

  1. Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India

    Rajesh Gopakumar & M. Vishal Anand

  2. Data Science and Computer Applications, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India

    Karunakar A. Kotegar

Authors
  1. Rajesh Gopakumar
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  2. Karunakar A. Kotegar
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  3. M. Vishal Anand
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Corresponding author

Correspondence to Rajesh Gopakumar .

Editor information

Editors and Affiliations

  1. Department of Computer Applications, Sikkim Manipal Institute of Technology, Rangpo, Sikkim, India

    Samarjeet Borah

  2. Department of Electrical Engineering, IIT Delhi, Delhi, India

    Tapan K. Gandhi

  3. Department of Computer Science, University of Milan, Milano, Milano, Italy

    Vincenzo Piuri

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Gopakumar, R., Kotegar, K.A., Vishal Anand, M. (2023). A Quantitative Study on the FaceNet System. In: Borah, S., Gandhi, T.K., Piuri, V. (eds) Advanced Computational and Communication Paradigms . ICACCP 2023. Lecture Notes in Networks and Systems, vol 535. Springer, Singapore. https://doi.org/10.1007/978-981-99-4284-8_17

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