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Fingerprint Liveness Detection Using Patch-Based Convolutional Neural Networks

패치기반 컨볼루션 뉴럴 네트워크 특징을 이용한 위조지문 검출

  • Received : 2016.09.30
  • Accepted : 2017.01.14
  • Published : 2017.02.28

Abstract

Nowadays, there have been an increasing number of illegal use cases where people try to fabricate the working hours by using fake fingerprints. So, the fingerprint liveness detection techniques have been actively studied and widely demanded in various applications. This paper proposes a new method to detect fake fingerprints using CNN (Convolutional Neural Ntworks) based on the patches of fingerprint images. Fingerprint image is divided into small square sized patches and each patch is classified as live, fake, or background by the CNN. Finally, the fingerprint image is classified into either live or fake based on the voting result between the numbers of fake and live patches. The proposed method does not need preprocessing steps such as segmentation because it includes the background class in the patch classification. This method shows promising results of 3.06% average classification errors on LivDet2011, LivDet2013 and LivDet2015 dataset.

Keywords

fingerprint liveness detection;CNN;fake fingerprint detection;presentation attack

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Acknowledgement

Grant : 스마트 디바이스용 박막 타입 지문센서 모듈 및 프라이버시 보호 응용 SW 기술개발

Supported by : 정보통신기술진흥센터