Low-cost face recognition system based on extended local binary pattern

Yon-Ping Chen, Qi Hui Chen, Kuan Yu Chou, Ren Hau Wu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

In recent years, the IoT application and the biometric-based authorization become popular. This paper proposes a face recognition system with high accuracy rate based on extended Local Binary Pattern, and applies it as an access control system on an IoT device which is always low-cost, low-power and small-footprint. The proposed face recognition system includes three parts, face detection, feature extraction and face recognition. For the face detection, the Viola-Jones face detector is adopted to find out the face information. The extended Local Binary Pattern then extracts the local features of the face. Further transform these features to a low-dimension subspace by Principle Component Analysis method. Finally, use the classification based on the sparse representation of L2 norm minimization to identify and verify the face. From the experimental results, the proposed method can achieve a high recognition rate better than 95% in several face databases, even reach 99% for the Cohn-Kanade face database. The access control system implemented on Raspberry Pi 3 is able to complete the whole face recognition in a second, which makes it indeed a real-Time system.

Original languageEnglish
Title of host publication2016 International Automatic Control Conference, CACS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages13-18
Number of pages6
ISBN (Electronic)9781509041084
DOIs
StatePublished - 10 Jul 2017
Event2016 International Automatic Control Conference, CACS 2016 - Taichung, Taiwan
Duration: 9 Nov 201611 Nov 2016

Publication series

Name2016 International Automatic Control Conference, CACS 2016

Conference

Conference2016 International Automatic Control Conference, CACS 2016
CountryTaiwan
CityTaichung
Period9/11/1611/11/16

Keywords

  • Face recognition
  • internet of things (IoT)
  • local binary pattern (LBP)
  • principal component analysis (PCA)
  • sparse representation (SRC)

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