Multi-race age estimation based on the combination of multiple classifiers

Kazuya Ueki, Masashi Sugiyama, Yasuyuki Ihara, Mitsuhiro Fujita

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

A considerable amount of research has been conducted on gender and age estimation from facial images over the last few years, and state-of-the-art technology has accomplished a practical accuracy level for a homogeneous race such as Japanese or Korean. However, achieving the same accuracy level across multiple races such as Caucasian, African American, and Hispanic is still highly challenging because of the strong diversity of the growth process of each race. Furthermore, difficulty of gathering training samples uniformly over various races and age brackets makes the problem even more challenging. In this paper, we propose a novel age estimation method that can overcome the above problems. Our method combines a recently proposed machine learning technique called Least-Squares Probabilistic Classifier (LSPC) with neural networks. Through large-scale real-world age estimation experiments, we demonstrate the usefulness of our proposed method.

Original languageEnglish
Title of host publication1st Asian Conference on Pattern Recognition, ACPR 2011
Pages633-637
Number of pages5
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event1st Asian Conference on Pattern Recognition, ACPR 2011 - Beijing, China
Duration: 2011 Nov 282011 Nov 28

Other

Other1st Asian Conference on Pattern Recognition, ACPR 2011
CountryChina
CityBeijing
Period11/11/2811/11/28

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ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Cite this

Ueki, K., Sugiyama, M., Ihara, Y., & Fujita, M. (2011). Multi-race age estimation based on the combination of multiple classifiers. In 1st Asian Conference on Pattern Recognition, ACPR 2011 (pp. 633-637). [6166681] https://doi.org/10.1109/ACPR.2011.6166681