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Design of a Contactless Access Security System using Palm Creases and Palm Vein Pattern Matching

손금과 정맥혈관 패턴매칭을 이용한 비접촉 출입 보안시스템 설계

  • Ki-Jung Kim (Dept. Computer Science, Hwasung Medi-Science University)
  • 김기중 (화성의과학대학교 컴퓨터사이언스학과)
  • Received : 2023.12.18
  • Accepted : 2024.02.17
  • Published : 2024.02.29

Abstract

In this paper, we developed a system with a near-infrared LED light source with a wavelength of 950nm to acquire palm vein images and a white LED light source to acquire palm creases based on Raspberry Pi. In addition, we implemented a unique pattern-extractable image processing technology that can prevent counterfeiting and enhance security of mixed creases and palmprints through image pre-processing (Gray scaling, Histogram Equalization, Blurring, Thresholding, Thinning) for the acquired vein and palm images, and secured a source technology that can be used in a security-enhanced system.

본 논문에서는 라즈베리파이 기반으로 손바닥 정맥혈관 이미지를 획득하기 위하여 950nm파장을 가지는 근적외선 LED 광원 장치와 손금을 획득하기 위한 백색 LED 광원 장치를 가지는 시스템을 개발하였다. 또한 획득한 정맥 및 손금 이미지에 대하여 영상 전처리 과정(흑백화, 평활화, 이진화, 블러링, 세선화 등)을 통하여 정맥과 손금이 혼합된 위조 방지 및 보안이 강화된 고유 패턴이 추출 가능한 영상처리 기술을 구현하여 보안성이 강화된 시스템에서 활용할 수 있는 원천 기술을 확보하였다.

Keywords

References

  1. S. Kim and W. Kim, "User Identification Method using Palm Creases and Veins based on Deep Learning," J. of Broadcast Engineering, vol. 23, no. 3, 2018, pp. 395-402.
  2. K. Ryu, "A Design of Palm Vein Authentication Algorithm Based on Biometic Date," Master's Thesis, Soongsil University Graduate School, 2020.
  3. P. Wang and D. Sun, "A research on palm vein recognition," IEEE 13th Int. Conf. on Signal Processing (ICSP), Chengdu, China, Nov. 2016, pp. 1347-1351.
  4. D. Jeong, J. Cho, and G. Kim, "Research commuter terminal system using finger vein," Proc. of the Korean Institute of Information and Commucation Sciences Conf., Jeju, Korea, Oct. 2016, pp. 672-673.
  5. K. Kim, "The Lines Extraction and Analysis of The Palm using Morphological Information of The Hand and Contour Tracking Method," J. of the Korea Institute of Electronic Communication Sciences, vol. 6, no. 2, 2011, pp. 243-248.
  6. Y. Kim, H. Kim, H. Nam, N. Lee, and Y. Ko, "A Study on the Autonomous Driving Algorithm Using Bluetooth and Rasberry Pi," J. of the Korea Institute of Electronic Communication Sciences, vol. 16, no. 4, 2021, pp. 689-698.
  7. H. Lee and Y. Park, "A Study of Attendance Check System using Face Recognition," J. of the Korea Institute of Electronic Communication Sciences, vol. 17, no. 6, 2022, pp. 1193-1198.
  8. W. J. Lee and Y. H. Lee, Raspberry Pi Home IoT, Seoul: Icbanq, 2022
  9. R. C. Gonzalez and R. E. Woods, Digital Image Processing 4th Ed.. New York: Pearson, 2017.
  10. M. K. Sarker, M. Song, H. Lee, and Y. Park, "Real-Time Vehicle License Plate Detection Based on Background Subtraction and Cascade of Boosted Classifiers," The J. of Korean Institute of Communications and Information Sciences, vol. 39C, no. 10, 2014, pp. 909-919.
  11. S. Sonil, R. Chadha, and S. Kaur, "A Review Paper on Thinning of Image Using Zhang and Suen Algorithm and Neural Network," J. of Computer Engineering (IOSR-JCE), vol. 18, no. 2, Mar-Apr. 2016, pp. 48-51.