On using the Self Organizing Map in Face Recognition

スポンサーリンク

概要

• 論文の詳細を見る

• The Self Organizing Map (SOM) is one of the most widely used neural network paradigm based on unsupervised competitive learning and can be utilized in a broad area of pattern recognition domain. Though SOM is used in some face recognition applications, it still did not utilized against pose and illumination variations in face based applications. In this paper we use SOM as a feature extractor and SVM as a feature recognizer to perform a complete face recognition system across pose and illumination. To validate our system we use the CMU-PIE database and make a comparison between our approach and other reported approach.

• 社団法人電子情報通信学会の論文
• 2007-02-15

著者

• Taniguchi Rin-ichiro

  Department of Advanced Information Technology, Kyushu University

• Sagheer Alaa

  Department Of Intelligent Systems Kyushu University

• TSURUTA Nayouki

  Department of Electronics Engineering and Computer Science, Fukuoka University

• MAEDA Sakashi

  Department of Electronics Engineering and Computer Science, Fukuoka University

• Taniguchi Rin-ichiro

  Department Of Intelligent Systems Kyushu University

• Taniguchi Rin-ichiro

  Department Of Advanced Information Technology Kyushu University

• Maeda Sakashi

  Department Of Electronics Engineering And Computer Science Fukuoka University

• Tsuruta Naoyuki

  Department Of Electronics Engineering And Computer Science Fukuoka University

• Tsuruta Nayouki

  Department Of Electronics Engineering And Computer Science Fukuoka University

• Taniguchi Rin-Ichiro

  Department of Intelligent Systems, Kyushu University

関連論文

• Hybrid Background Modeling for Long-term and Short-term Illumination Changes
• Hybrid background modeling for long-term and short-term illumination changes (特集 ビジョン技術によるイノベーション)
• A Randomized Self-Organizing Maps for Gesture Recognition (論文特集 自己組織化マップとその応用)
• 変形股関節を対象とした多階層統計的形状モデルによる3次元CT画像からの大腿骨領域の半自動抽出
• On using the Self Organizing Map in Face Recognition
• Fast Learning Approach Using Self Organizing Map(一般セッション(1),文字認識・文書理解)
• Fast Learning Approach Using Self Organizing Map(一般セッション(1), 文字認識・文書理解)
• Shape and Pose Parameter Acquisition of 3D Multi-Part Objects on Multiple Viewpoint Image Sequences
• Face recognition across pose using self-organizing maps (ニューロコンピューティング)

スポンサーリンク