LBPH based improved face recognition at low resolution

Aftab Ahmed, Jiandong Guo, Fayaz Ali, Farha Deeba, Awais Ahmed

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

52 Scopus citations

Abstract

Automatic individual face recognition is the most challenging query from the past decade in computer vision. However, the law enforcement agencies are inadequate to identify and recognize any person through the video monitoring cameras further efficiently; the blur conditions, illumination, resolution, and lighting are still the major problems in face recognition. Our proposed system operates better at the minimum low resolution of 35px to identify the human face in various angles, side poses and tracking the face during human motion. We have designed the dataset (LR500) for training and classification. This paper employs the Local Binary Patterns Histogram (LBPH) algorithm architecture to address the human face recognition in real time at the low level of resolution.

Original languageBritish English
Title of host publication2018 International Conference on Artificial Intelligence and Big Data, ICAIBD 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages144-147
Number of pages4
ISBN (Electronic)9781538669877
DOIs
StatePublished - 25 Jun 2018
Event2018 International Conference on Artificial Intelligence and Big Data, ICAIBD 2018 - Chengdu, China
Duration: 26 May 201828 May 2018

Publication series

Name2018 International Conference on Artificial Intelligence and Big Data, ICAIBD 2018

Conference

Conference2018 International Conference on Artificial Intelligence and Big Data, ICAIBD 2018
Country/TerritoryChina
CityChengdu
Period26/05/1828/05/18

Keywords

  • Face recognition
  • Feature extraction
  • LBPH
  • Low resolution

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