Learning a lightweight deep convolutional network for joint age and gender recognition

Linnan Zhu, Keze Wang, Liang Lin, Lei Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

8 Citations (Scopus)


This paper proposes a lightweight deep model to recognize age and gender from a face image. Though simple, our network architecture is able to complete the two tasks effectively and efficiently. Moreover, different from existing methods, we simultaneously perform the age and gender recognition tasks via a joint regression model. Specifically, our model employs a multi-task learning scheme to learn shared features for these two correlated tasks in an end-to-end manner. Extensive experimental results on the recent Adience benchmark demonstrate that our model achieves competitive recognition accuracy with the state-of-the-art methods but with much faster speed, i.e., about 10 times faster in the testing phase. Our model can be easily adopted and extended to other facial applications.

Original languageEnglish
Title of host publication2016 23rd International Conference on Pattern Recognition, ICPR 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781509048472
Publication statusPublished - 13 Apr 2017
Event23rd International Conference on Pattern Recognition, ICPR 2016 - Cancun Center, Cancun, Mexico
Duration: 4 Dec 20168 Dec 2016

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Conference23rd International Conference on Pattern Recognition, ICPR 2016

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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