한국멀티미디어학회논문지 (Journal of Korea Multimedia Society)

  • 제16권9호
  • /
  • Pages.1057-1066
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  • 2013
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  • 1229-7771(pISSN)
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  • 2384-0102(eISSN)
한국멀티미디어학회 (Korea Multimedia Society)
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특징점간의 벡터 유사도 정합을 이용한 손가락 관절문 인증

Finger-Knuckle-Print Verification Using Vector Similarity Matching of Keypoints

  • 김민기 (경상대학교 컴퓨터과학과 컴퓨터정보통신연구소)
  • 투고 : 2013.07.08
  • 심사 : 2013.08.27
  • 발행 : 2013.09.30
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⟨ 이전 논문 다음 논문 ⟩

초록

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.

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.

키워드

참고문헌

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피인용 문헌

  1. Individual Identification Using Ear Region Based on SIFT vol.18, pp.1, 2015, https://doi.org/10.9717/kmms.2015.18.1.001
  2. Performance Analysis of Modified LLAH Algorithm under Gaussian Noise vol.18, pp.8, 2015, https://doi.org/10.9717/kmms.2015.18.8.901