Performance evaluation of face attribute estimation method using DendroNet

Hiroya Kawai, Koichi Ito, Takafumi Aoki

研究成果: Conference contribution

抄録

There are many studies on face recognition, which identifies a person using distinctive features extracted from a face image. One of the problems in face recognition is that the accuracy of face recognition decreases due to environmental changes such as head pose, emotion, illumination, etc. Addressing this problem, soft biometrics, which uses attributes such as age and gender for person authentication, is expected to improve the accuracy of face recognition. This paper proposes a face attribute estimation method using the Convolutional Neural Network (CNN). The CNN architecture of the proposed method, called DendroNet, is automatically designed according to the relationships among attributes. Though experiments using the CelebA dataset, we demonstrate that the proposed method exhibits better performance than conventional methods.

本文言語English
ホスト出版物のタイトル2019 IEEE 8th Global Conference on Consumer Electronics, GCCE 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ページ184-185
ページ数2
ISBN(電子版)9781728135755
DOI
出版ステータスPublished - 2019 10
イベント8th IEEE Global Conference on Consumer Electronics, GCCE 2019 - Osaka, Japan
継続期間: 2019 10 152019 10 18

出版物シリーズ

名前2019 IEEE 8th Global Conference on Consumer Electronics, GCCE 2019

Conference

Conference8th IEEE Global Conference on Consumer Electronics, GCCE 2019
CountryJapan
CityOsaka
Period19/10/1519/10/18

ASJC Scopus subject areas

  • Instrumentation
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Electrical and Electronic Engineering

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