콘텐츠 본문
논문 해외 국제전문학술지(SCI급) DeepPore: Fingerprint Pore Extraction Using Deep Convolutional Neural Networks
- 학술지 구분 국제전문학술지(SCI급)
- 게재년월 2017-12
- 저자명 장한얼
- 학술지명 IEEE SIGNAL PROCESSING LETTERS
- 발행처명 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- 발행국가 해외
- 논문언어 외국어
- 전체저자수 5
논문 초록 (Abstract)
As technological developments have enabled high-quality fingerprint scanning, sweat pores, one of the Level 3 features of fingerprints, have been successfully used in automatic fingerprint recognition systems (AFRS). Since the pore extraction process is a critical step for AFRS, high accuracy is required. However, it is difficult to extract the pore correctly because the pore shape depends on the person, region, and pore type. To solve the problem, we have presented a pore extraction method using deep convolutional neural networks and pore intensity refinement. The deep networks are used to detect pores in detail using a large area of a fingerprint image. We then refine the pore information by finding local maxima to identify pores with different intensities in the fingerprint image. The experimental results show that our pore extraction method performs better than the state-of-the-art methods.
