Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/75547
Title: Learning a lightweight deep convolutional network for joint age and gender recognition
Authors: Zhu, LN 
Wang, KZ 
Lin, L
Zhang, L 
Issue Date: 2016
Publisher: IEEE Computer Society
Source: 23rd International Conference on Pattern Recognition (ICPR), Mexican Assoc Comp Vis Robot & Neural Comp, Mexico, Dec 4-8, 2016, p. 3282-3287 How to cite?
Abstract: 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.
URI: http://hdl.handle.net/10397/75547
ISBN: 978-1-5090-4847-2
ISSN: 1051-4651
Appears in Collections:Conference Paper

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