The Journal of the Convergence on Culture Technology (문화기술의 융합)

The International Promotion Agency of Culture Technology (국제문화기술진흥원)
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Research Trends in CNN-based Fingerprint Classification

CNN 기반 지문분류 연구 동향

  • Jung, Hye-Wuk (Dept. of College of Liberal Arts and Interdisciplinary Studies, Kyonggi University)
  • 정혜욱 (경기대학교 진성애교양대학 교양학부)
  • Received : 2022.07.31
  • Accepted : 2022.09.08
  • Published : 2022.09.30
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Abstract

Recently, various researches have been made on a fingerprint classification method using Convolutional Neural Networks (CNN), which is widely used for multidimensional and complex pattern recognition such as images. The CNN-based fingerprint classification method can be executed by integrating the two-step process, which is generally divided into feature extraction and classification steps. Therefore, since the CNN-based methods can automatically extract features of fingerprint images, they have an advantage of shortening the process. In addition, since they can learn various features of incomplete or low-quality fingerprints, they have flexibility for feature extraction in exceptional situations. In this paper, we intend to identify the research trends of CNN-based fingerprint classification and discuss future direction of research through the analysis of experimental methods and results.

최근 이미지와 같은 다차원의 복잡한 패턴 인식에 많이 사용하는 CNN(Convolutional Neural Networks)을 적용한 지문분류 방법이 다양하게 연구되고 있다. CNN 기반 지문분류 방법은 일반적으로 특징추출과 분류 단계로 나누어진 두 단계의 과정을 하나로 통합하여 실행할 수 있다. 따라서 CNN 기반 방법은 지문 이미지의 특징을 자동으로 추출할 수 있으므로, 처리 과정을 단축시킬 수 있는 장점이 있다. 또한 불완전하거나 품질이 낮은 지문의 특징을 다양하게 학습할 수 있으므로, 예외 상황의 특징 추출에 대해 유연성이 있다. 본 논문에서는 CNN 기반 지문분류연구동향을 파악하고, 실험 방법 및 결과 분석을 통해 향후 연구방향에 대해 논의하고자 한다.

Keywords

Acknowledgement

이 논문은 대한민국 교육부와 한국연구재단의 2017년도 이공학 개인기초연구지원사업(No. 2015R1D1A1A01061064)과 2021년도 인문사회분야 신진연구자지원사업(NR F-2020S1A5A8042850)의 지원을 받아 수행된 연구임.

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