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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.

Acknowledgement

Grant : ICBMS 플랫폼 간 정보모델 연동 및 서비스 매쉬업을 위한 스마트 중재 기술 개발

Supported by : 정보통신기술진흥센터, 한국연구재단

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.