Age-invariant person identification by segmentation verification of face image

Yuta Somada, Wataru Oyama, Tetsushi Wakabayashi

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

Face recognition has been a major research theme over the last two decades. There are several problems to be solved to improve the performance of face recognition. Such major problems involve appearance variation due to pose, illumination, expression, and aging. In particular, aging includes internal and external factors that cause facial appearance variation and, consequently, it is the most difficult problem to handle. In this paper, we propose a face recognition method that is robust against facial appearance variation due to aging. The proposed method employs segmentation verification of frontal face images that consists of the following three steps. (1) Face image segmentation generates three regional subimages from the input face image. (2) A matching score is calculated using gradient features from a pair consisting of the input image and a registered image for each of the three generated subimages and original (whole face) image. We obtain four matching scores. (3) The verifying classifier evaluates the matching score vector formed of the matching scores calculated for each of the four images and predicts the a posteriori probability that two matching images belong to the same person. The results of an experimental evaluation with the FGNET and MORPH face aging datasets clarify the effectiveness of the proposed method for age invariant face recognition

本文言語英語
ホスト出版物のタイトルProceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017
出版社Institute of Electrical and Electronics Engineers Inc.
ページ364-369
ページ数6
ISBN(電子版)9781538633540
DOI
出版ステータス出版済み - 12 13 2018
外部発表はい
イベント4th Asian Conference on Pattern Recognition, ACPR 2017 - Nanjing, 中国
継続期間: 11 26 201711 29 2017

出版物シリーズ

名前Proceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017

その他

その他4th Asian Conference on Pattern Recognition, ACPR 2017
国/地域中国
CityNanjing
Period11/26/1711/29/17

All Science Journal Classification (ASJC) codes

  • 人工知能
  • コンピュータ ビジョンおよびパターン認識
  • 信号処理

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