Age and gender recognition using multi-task CNN

Duc Quang Vu, Thi Thu Trang Phung, Chien Yao Wang, Jia Ching Wang

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

Abstract

The investigation into age and gender identification has been receiving more attention from researchers since social and multimedia networks are becoming more popular nowadays. Recently published methods have yielded quite good results in terms of accuracy but have also proven to be ineffective in realtime applications because the models were too complicated. In this paper, we propose a lightweight model that can classify both age and gender. The number of parameters used in this model is 5 times less than existing models. Experiment results show that the accuracy of the proposed method is equivalent to state-of-the-art methods, while the speed of age and gender recognition decreases by 4 times on the Audience benchmark.

Original languageEnglish
Title of host publication2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1937-1941
Number of pages5
ISBN (Electronic)9781728132488
DOIs
StatePublished - Nov 2019
Event2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019 - Lanzhou, China
Duration: 18 Nov 201921 Nov 2019

Publication series

Name2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019

Conference

Conference2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019
Country/TerritoryChina
CityLanzhou
Period18/11/1921/11/19

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