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

Kazuya Ueki*, Masashi Sugiyama, Yasuyuki Ihara, Mitsuhiro Fujita

*この研究の対応する著者

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル1st Asian Conference on Pattern Recognition, ACPR 2011
ページ633-637
ページ数5
DOI
出版ステータスPublished - 2011
外部発表はい
イベント1st Asian Conference on Pattern Recognition, ACPR 2011 - Beijing, China
継続期間: 2011 11月 282011 11月 28

Other

Other1st Asian Conference on Pattern Recognition, ACPR 2011
国/地域China
CityBeijing
Period11/11/2811/11/28

ASJC Scopus subject areas

  • コンピュータ ビジョンおよびパターン認識

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