DOI QR코드

DOI QR Code

Development of a Visitor Recognition System Using Open APIs for Face Recognition

얼굴 인식 Open API를 활용한 출입자 인식 시스템 개발

  • Received : 2016.08.12
  • Accepted : 2017.01.24
  • Published : 2017.04.30

Abstract

Recently, as the interest rate and necessity for security is growing, the demands for a visitor recognition system are being increased. In order to recognize a visitor in visitor recognition systems, the various biometric methods are used. In this paper, we propose a visitor recognition system based on face recognition. The visitor recognition system improves the face recognition performance by integrating several open APIs as a single algorithm and by performing the ensemble of the recognition results. For the performance evaluation, we collected the face data for about five months and measured the performance of the visitor recognition system. As the results of the performance measurement, the visitor recognition system shows a higher face recognition rate than using a single face recognition API, meeting the requirements on performance.

최근 보안에 대한 관심과 필요성이 증가하면서 출입자 인식 시스템의 수요가 증대되고 있다. 출입자 인식 시스템은 출입자를 인식하기 위해서 다양한 생체인식 방법을 사용하고 있다. 본 논문에서는 다양한 특성과 강점을 가진 다수의 얼굴인식 Open API 서비스를 통합하고, 그 인식결과를 앙상블 함으로써 인식 성능을 개선하는 얼굴인식 기반 출입자 인식 시스템을 제안한다. 또한 다양한 얼굴 인식 Open API 서비스를 앙상블 하는 출입자 인식 시스템의 구조를 제안한다. 성능 측정은 약 5개월 간 수집한 얼굴 데이터를 이용하여 수행하였으며, 측정결과로 본 논문에서 제안하는 출입자 인식 시스템이 단일 얼굴인식 Open API 서비스를 사용했을 때보다 더 높은 얼굴인식률을 보임을 확인하였다.

Keywords

References

  1. R. Jafri and H. R. Arabnia, "A survey of face recognition techniques," Information Processing Systems, Vol.5, No.2, pp.41-68, 2009. https://doi.org/10.3745/JIPS.2009.5.2.041
  2. Inttelix, Inttelix [Internet], http://www.inttelix.com.
  3. TCIT, TCIT [Internet], http://www.tcit-us.com.
  4. Digiface, Digiface [Internet], http://www.digiface.com.br.
  5. FIRSTEC, FIRSTEC [Internet], http://www.firsteccom.co.kr.
  6. VS-KOREA, Smart-Face [Internet], http://www.vs-korea.com.
  7. Lambda Labs, Lambda Labs [Internet], https://lambdal.com /face-recognition-api.
  8. Betaface, Betaface API [Internet], https://betafaceapi.com.
  9. Kairos, Kairos [Internet], https://www.kairos.com.
  10. Face++, Face++ [Internet], https://www.faceplusplus.com.
  11. S. Z. Li and A. K. Jain, Handbook of face recognition, 2nd ed. Springer, 2011.
  12. C. Pagano, E. Granger, R. Sabourin, A. Rattani, G. L. Marcialis, and F. Roli, "Efficient adaptive face recognition systems based on capture conditions," in Proceedings of Computational Intelligence in Biometrics and Identity Management, pp.60-67, 2014.
  13. L. Wen, G. Guo, and X. Li, "A study on the influence of body weight changes on face recognition," in Proceedings of IEEE International Joint Conference on Biometrics, pp. 1-6, 2014.
  14. T. Kim, H. Park, S. H. Hong, and Y. Chung, "Integrated system of face recognition and sound localization for a smart door phone," IEEE Transactions on Consumer Electronics, Vol.59, No.3, pp.598-603, 2013. https://doi.org/10.1109/TCE.2013.6626244
  15. M. A. H. Lucas, L. A. Luis, E. B. M. Maria, R. Mariano, T. Juliana, and G. Sergio, "Smart doorbell: An ICT solution to enhance inclusion of disabled people," in Proceedings of ITU Kaleidoscope Trust in the Information Society, pp.1-7, 2015.
  16. K. H. Kwon and H. B. Lee, "Gate Management System by Face Recognition using Smart Phone," The Korea Society of Computer and Information, Vol.16, No.11, pp.9-15, 2011.
  17. G. D. Thomas, "Ensemble Methods in Machine Learning," Multiple Classifier Systems, Vol.1857, pp.1-5, 2000.
  18. K. H. Tin, "Random decision forests," in Proceedings of Document Analysis and Recognition, pp.278-282. 1995.
  19. G. Ratsch, T. Onoda, and K. R. Muller, "Soft margins for AdaBoost," Machine Learning, Vol.42, No.3, pp.287-320, 2001. https://doi.org/10.1023/A:1007618119488
  20. H. M. Tang, M. R. Lyu, and I. King, "Face recognition committee machine," in Proceedings of International Conference on Multimedia and Expo, Vol.3, pp.425-428, 2003.