Design and Implementation of a Face Authentication System (딥러닝 기반의 얼굴인증 시스템 설계 및 구현)

Lee, Seungik (이승익)

Design and Implementation of a Face Authentication System

Journal of Software Assessment and Valuation
Abbr : JSAV
2020, 16(2), pp.63-68
Publisher : Korea Software Assessment and Valuation Society
Research Area : Engineering > Computer Science
Received : August 10, 2020
Accepted : December 21, 2020
Published : December 31, 2020

Lee, Seungik ¹

¹연암공과대학교

Candidate

ABSTRACT

This paper proposes a face authentication system based on deep learning framework. The proposed system is consisted of face region detection and feature extraction using deep learning algorithm, and performed the face authentication using joint-bayesian matrix learning algorithm. The performance of proposed paper is evaluated by various face database , and the face image of one person consists of 2 images. The face authentication algorithm was performed by measuring similarity by applying 2048 dimension characteristic and combined Bayesian algorithm through Deep Neural network and calculating the same error rate that failed face certification. The result of proposed paper shows that the proposed system using deep learning and joint bayesian algorithms showed the equal error rate of 1.2%, and have a good performance compared to previous approach.

KEYWORDS

deep learning, face authentication, image processing, joint bayesian, active shape model

Citation status

* References for papers published after 2023 are currently being built.

[confproc] Y. Taigman / 2014 / Deepface: Closing the gap to human-level performance in face verification / Proceedings of the IEEE conference on computer vision and pattern recognition : 1701~1708

[journal] 김동주 / 2017 / Implementation of a Face Authentication Embedded System Using High-dimensional Local Binary Pattern Descriptor and Joint Bayesian Algorithm / 한국정보통신학회논문지 / 한국정보통신학회 21(9) : 1674~1680

[confproc] W. Yu / 2016 / A depth cascade face detection algorithm based on adaboost / Proceeding of IEEE International Conference on Network Infrastructure and Digital Content : 103~107

[confproc] J. Redmon / 2016 / You only look once: Unified, real-time object detection / Proceedings of the IEEE conference on computer vision and pattern recognition : 779~788

[confproc] D. Chen / 2013 / Blessing of dimensionality: Highdimensional feature and its efficient compression for face verification / Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition : 3025~3032

[web] S. Zagoruyko / 2016 / “Wide residual networks”, arXiv preprint arXiv:1605.07146 / https://arxiv.org/abs/1605.07146

[journal] 정광민 / 2012 / Face classification and analysis based on geometrical feature of face / 한국정보통신학회논문지 / 한국정보통신학회 16(7) : 1495~1504

[confproc] D. Chen / 2012 / Bayesian face revisited: A joint formulation / Proceeding of European Conference on Computer Vision : 566~579

[web] R. Girshick / 2015 / Fast r-cnn / ICCV / https://openaccess.thecvf.com/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf

[journal] Shaoqing Ren / 2017 / Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks / IEEE Transactions on Pattern Analysis and Machine Intelligence / Institute of Electrical and Electronics Engineers (IEEE) 39(6) : 1137~1149

[confproc] W Liu / 2016 / SSD: Single Shot MultiBox Detector / European Conference on Computer Vision : 21~37

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Journal of Software Assessment and Valuation 2023 KCI Impact Factor : 0.35