Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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Finger-Knuckle-Print Verification Using Vector Similarity Matching of Keypoints

특징점간의 벡터 유사도 정합을 이용한 손가락 관절문 인증

  • 김민기 (경상대학교 컴퓨터과학과 컴퓨터정보통신연구소)
  • Received : 2013.07.08
  • Accepted : 2013.08.27
  • Published : 2013.09.30
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Abstract

Personal verification using finger-knuckle-print(FKP) uses lines and creases at the finger-knuckle area, so the orientation information of texture is an important feature. In this paper, we propose an effective FKP verification method which extracts keypoints using SIFT algorithm and matches the keypoints by vector similarity. The vector is defined as a direction vector which connects a keypoint extracted from a query image and a corresponding keypoint extracted from a reference image. Since the direction vector is created by a pair of local keypoints, the direction vector itself represents only a local feature. However, it has an advantage of expanding a local feature to a global feature by comparing the vector similarity among vectors in two images. The experimental results show that the proposed method is superior to the previous methods based on orientation codes.

손가락 관절문(FKP, finger-knuckle-print)을 이용한 개인 인증은 손가락 관절부에 나타나는 주름의 특징을 이용하는 것으로, 텍스처의 방향 정보가 중요한 특징이 된다. 본 논문에서는 SIFT 알고리즘을 이용하여 특징점들을 추출하고, 벡터 유사도 정합을 통해 FKP를 효과적으로 인증할 수 있는 방법을 제안하다. 벡터는 질의 영상에서 추출한 특징점과 이에 대응되는 참조 영상의 특징점을 연결하는 방향 벡터로 정의된다. 국소적인 특징점 쌍으로부터 방향 벡터를 생성하기 때문에 방향 벡터 자체는 국소적인 특징만을 나타내지만, 두 영상 간에 존재하는 다른 벡터들 간의 유사도를 비교함으로써 전역적인 특징으로 확장되는 장점이 있다. 실험결과 제안하는 방법은 기존의 방향코드를 이용한 다양한 방식에 비하여 우수한 성능을 나타내었다.

Keywords

References

  1. K. Delac and M. Grgic, "A Survey of Biometric Recognition Methods," Proc. of International Symposium Electronics in Marine , pp. 184-193, 2004.
  2. A.H. Mir, S. Rubab, and Z.A. Jhat, "Biometrics Verification: A Literature Survey," International Journal of Computing and ICT Research, Vol. 5, No. 2, pp. 67-80, 2011.
  3. 김희승, 배병규, "손가락 면 영상 판별에 의한 개인 식별 연구," 멀티미디어학회논문지, 제13 권, 제3호, pp. 378-391, 2010.
  4. A. Kong and D. Zhang, "Competitive Coding Scheme for Palmprint Verification," Proc. of 17th ICPR, pp. 520-523, 2004.
  5. L. Zhang, L. Zhang, D. Zhang, and H. Zhu, "Online Finger-knuckle-print Verification for Personal Authentication," Pattern Recognition, Vol. 43, No. 7, pp. 2560-2571, 2010. https://doi.org/10.1016/j.patcog.2010.01.020
  6. L. Zhang, L. Zhang, and D. Zhang, "MonogenicCode: A Novel Fast Feature Coding Algorithm with Applications to Finger- Knuckle-Print Recognition," Proc. of International Workshop on Emerging Techniques and Challenges for Hand-Based Biometrics, pp. 1-4, 2010.
  7. A. Kumar and Y. Zhou, "Personal Identification using finger knuckle orientation features," Electronics Letters, Vol. 45, No. 20, pp. 1023-1025, 2009. https://doi.org/10.1049/el.2009.1435
  8. Z. Li, K. Wang, and W. Zuo, "Finger-Knuckle- Print Recognition using Local Orientation Feature Based on Steerable Filter," Communications in Computer and Information Science, Vol. 304, pp. 224-230, 2012. https://doi.org/10.1007/978-3-642-31837-5_33
  9. S. Aoyama, K. Ito, and T. Aoki, "Fingerknuckle- print Recognition using BLPOCbased local block matching," Proc. of the Asian Conference on Pattern Recognition, pp. 525-529, 2011.
  10. L. Zhang, L. Zhang, D. Zhang, and H. Zhu, "Ensemble of Local and Global Information for Finger-knuckle-print Recognition," Pattern Recognition, Vol. 44, No. 9, pp. 1990-1998, 2011. https://doi.org/10.1016/j.patcog.2010.06.007
  11. A. Morales, C.M. Travieso, M.A. Ferrer, and J.B. Alonso, "Improved Finger-knuckle-print Authentication Based on Orientation Enhancement," Electronics Letters, Vol. 47, No. 6, pp. 380-381, 2011. https://doi.org/10.1049/el.2011.0156
  12. D.G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," International Journal of Computer Vision, Vol. 60, No. 2, pp. 91-110, 2004. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  13. Z. Le-qing, "Finger Knuckle Print Recognition Based on SURF Algorithm," Proc. of International Conference on Fuzzy Systems and Knowledge Discovery, pp. 1879- 1883, 2011.
  14. H. Bay, A. Ess, T. Tuyellaars, and L.V. Gool, "Speeded-Up Robust Features (SURF)," Computer Vision and Image Understanding, Vol. 110, No. 3, pp. 346-359, 2008. https://doi.org/10.1016/j.cviu.2007.09.014
  15. G.S. Badrinath, A. Nigam, and P. Gupta, "An Efficient Finger-knuckle-print Based on Recognition System Fusing SIFT and SURF Matching Scores," Proc. of International Conference on Information and Communications Security, pp. 374-387, 2011.
  16. http://www4.comp.polyu.edu.hk/-csajaykr/ IITD/iitd_knuckle.htm, IIT Delhi Finger Knuckle Database Version 1.0, 2009.
  17. A. Kumar and C. Ravikanth, "Personal Authentication using Finger Knuckle Surface," IEEE Trans. on Information Forensics and Security, Vol. 4, No. 1, pp. 98-110, 2009. https://doi.org/10.1109/TIFS.2008.2011089

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