Using Correlated Regression Models to Calculate Cumulative Attributes for Age Estimation

Lili PAN  Qiangsen HE  Yali ZHENG  Mei XIE  

IEICE TRANSACTIONS on Information and Systems   Vol.E98-D   No.12   pp.2349-2352
Publication Date: 2015/12/01
Publicized: 2015/08/28
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2015EDL8158
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
cumulative attributes,  gender-specific age estimation,  

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Facial age estimation requires accurately capturing the mapping relationship between facial features and corresponding ages, so as to precisely estimate ages for new input facial images. Previous works usually use one-layer regression model to learn this complex mapping relationship, resulting in low estimation accuracy. In this letter, we propose a new gender-specific regression model with a two-layer structure for more accurate age estimation. Different from recent two-layer models that use a global regressor to calculate cumulative attributes (CA) and use CA to estimate age, we use gender-specific ones to calculate CA with more flexibility and precision. Extensive experimental results on FG-NET and Morph 2 datasets demonstrate the superiority of our method over other state-of-the-art age estimation methods.