Face recognition using scattering convolutional network

Shervin Minaee, Amirali Abdolrashidi, Yao Wang

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

Abstract

Face recognition has been an active research area in the past few decades. In general, face recognition can be very challenging due to variations in viewpoint, illumination, facial expression, etc. Therefore it is essential to extract features which are invariant to some or all of these variations. Here a new image representation, called scattering trans-form/network, has been used to extract features from faces. The scattering transform is a kind of convolutional network which provides a powerful multi-layer representation for signals. After extraction of scattering features, PCA is applied to reduce the dimensionality of the data and then a multi-class support vector machine is used to perform recognition. The proposed algorithm has been tested on three face datasets and achieved a very high recognition rate.

Original languageEnglish (US)
Title of host publication2017 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781538648735
DOIs
StatePublished - Jul 1 2017
Event2017 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2017 - Philadelphia, United States
Duration: Dec 2 2017 → …

Publication series

Name2017 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2017 - Proceedings
Volume2018-January

Other

Other2017 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2017
Country/TerritoryUnited States
CityPhiladelphia
Period12/2/17 → …

ASJC Scopus subject areas

  • Signal Processing
  • Cardiology and Cardiovascular Medicine
  • Health Informatics
  • Clinical Neurology

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