Feature map sharing hypercolumn model for shift invariant face recognition

Saleh Aly, Naoyuki Tsuruta, Rin-Ichiro Taniguchi

研究成果: ジャーナルへの寄稿学術誌査読

抄録

In this article, we propose a shift-invariant pattern recognition mechanism using a feature-sharing hypercolumn model (FSHCM). To improve the recognition rate and to reduce the memory requirements of the hypercolumn model (HCM), a shared map is constructed to replace a set of local neighborhood maps in the feature extraction and feature integration layers. The shared maps increase the ability of the network to deal with translation and distortion variations in the input image. The proposed face recognition system employed a FSHCM neural network to perform feature extraction and use a linear support vector machine for a recognition task. The effectiveness of the proposed approach is verified by measuring the recognition accuracy using the misaligned ORL face database.

本文言語英語
ページ(範囲)271-274
ページ数4
ジャーナルArtificial Life and Robotics
14
2
DOI
出版ステータス出版済み - 11月 1 2009

!!!All Science Journal Classification (ASJC) codes

  • 生化学、遺伝学、分子生物学(全般)
  • 人工知能

フィンガープリント

「Feature map sharing hypercolumn model for shift invariant face recognition」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル