Journal of Broadcast Engineering (방송공학회논문지)

The Korean Institute of Broadcast and Media Engineers (한국방송∙미디어공학회)
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User Identification Method using Palm Creases and Veins based on Deep Learning

손금과 손바닥 정맥을 함께 이용한 심층 신경망 기반 사용자 인식

  • Kim, Seulbeen (Department of Electrical and Electronic Engineering, Konkuk University) ;
  • Kim, Wonjun (Department of Electrical and Electronic Engineering, Konkuk University)
  • 김슬빈 (건국대학교 전기전자공학부) ;
  • 김원준 (건국대학교 전기전자공학부)
  • Received : 2018.03.30
  • Accepted : 2018.05.03
  • Published : 2018.05.30
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Abstract

Human palms contain discriminative features for proving the identity of each person. In this paper, we present a novel method for user verification based on palmprints and palm veins. Specifically, the region of interest (ROI) is first determined to be forced to include the maximum amount of information with respect to underlying structures of a given palm image. The extracted ROI is subsequently enhanced by directional patterns and statistical characteristics of intensities. For multispectral palm images, each of convolutional neural networks (CNNs) is independently trained. In a spirit of ensemble, we finally combine network outputs to compute the probability of a given ROI image for determining the identity. Based on various experiments, we confirm that the proposed ensemble method is effective for user verification with palmprints and palm veins.

손바닥은 손금, 정맥 등 고유한 특징 정보를 포함하고 있는 신체 부위로 이를 이용한 다양한 사용자 인식 방법이 지속적으로 연구되어 왔다. 본 논문에서는 손금과 손바닥 정맥을 함께 이용한 사용자 인식 방법을 제안한다. 먼저, 손바닥 영역에서 손금과 정맥이 가장 많이 포함되어 있는 관심 영역을 검출하고, 에지 방향성 및 밝기 통계정보를 이용하여 정맥 영상 화질 개선을 수행한다. 이후 다중 스펙트럼 환경에서 획득된 복수의 영상을 각각 독립된 심층 신경망의 입력으로 이용하여 손금과 정맥 패턴을 효과적으로 학습한다. 다양한 상황에서의 실험을 통해 본 논문에서 제안하는 방법이 기존 사용자 인식 방법 대비 개선된 결과를 보임을 확인하고 그 결과를 분석한다.

Keywords

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