An efficient dense network for semantic segmentation of eyes images captured with virtual reality lens

Andres Valenzuela, Claudia Arellano, Juan Tapia

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

3 Scopus citations

Abstract

Eye-tracking and Gaze estimation are difficult tasks that may be used for several applications including human-computer interfaces, salience detection and Virtual reality amongst others. This paper presents a segmentation algorithm based on deep learning that efficiently discriminates pupils, iris, and sclera from the background in images captured using a Virtual Reality lens. A light network called DensetNet10 trained from scratch is proposed. It contains fewer parameters than traditional architectures based on DenseNet which allows it to be used in mobile device applications. Experiments show that this network achieved higher IOU rates when comparing with DensetNet56-67-103 and DeeplabV3+ models in the Facebook database. Furthermore, this method reached 8th place in The Facebook semantic segmentation challenge with 0.94293 mean IOU and 202.084 parameters with a final score of 0.97147.

Original languageEnglish
Title of host publicationProceedings - 15th International Conference on Signal Image Technology and Internet Based Systems, SISITS 2019
EditorsKokou Yetongnon, Albert Dipanda, Gabriella Sanniti di Baja, Luigi Gallo, Richard Chbeir
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages28-34
Number of pages7
ISBN (Electronic)9781728156866
DOIs
StatePublished - Nov 2019
Event15th International Conference on Signal Image Technology and Internet Based Systems, SISITS 2019 - Sorrento, Italy
Duration: 26 Nov 201929 Nov 2019

Publication series

NameProceedings - 15th International Conference on Signal Image Technology and Internet Based Systems, SISITS 2019

Conference

Conference15th International Conference on Signal Image Technology and Internet Based Systems, SISITS 2019
Country/TerritoryItaly
CitySorrento
Period26/11/1929/11/19

Keywords

  • Biometrics
  • Facebook Challengue
  • Semantic Segmentation

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