Fingerprint recognition using translation invariant scattering network

Shervin Minaee, Yao Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Fingerprint recognition has drawn a lot of attention during the last few decades. Different features and algorithms have been used for fingerprint recognition in the past. In this paper, a powerful image representation called scattering transform/ network is used for recognition. Scattering network is a convolutional network where its architecture and filters are predefined wavelet transforms. The first layer of scattering representation is similar to SIFT descriptors and the higher layers capture higher frequency content of the signal. After extracting the scattering features, their dimensionality is reduced by applying principal component analysis (PCA). In the end, multi-class SVM is used to perform template matching for the recognition task. The proposed algorithm in this paper is one of the first works which explores the application of deep architecture for fingerprint recognition. The proposed scheme is tested on a well-known fingerprint database and has shown promising results with the best accuracy rate of 98%.

Original languageEnglish (US)
Title of host publication2015 IEEE Signal Processing in Medicine and Biology Symposium - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509013500
DOIs
StatePublished - Feb 11 2016
EventIEEE Signal Processing in Medicine and Biology Symposium - Philadelphia, United States
Duration: Dec 12 2015 → …

Publication series

Name2015 IEEE Signal Processing in Medicine and Biology Symposium - Proceedings

Conference

ConferenceIEEE Signal Processing in Medicine and Biology Symposium
CountryUnited States
CityPhiladelphia
Period12/12/15 → …

ASJC Scopus subject areas

  • Biomedical Engineering
  • Signal Processing
  • Radiology Nuclear Medicine and imaging
  • Health Informatics

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