An Illumination Invariant Bimodal Method Employing Discriminant Features for Face Recognition

スポンサーリンク

概要

• 論文の詳細を見る

• A novel bimodal method for face recognition under low-level lighting conditions is proposed. It fuses an enhanced gray level imageand an illumination-invariant geometric image at the feature-level. To further improve the recognition performance under large variations in attributions such as poses and expressions, discriminant features are extracted from source images using the wavelet transform-based method. Features are adaptively fused to reconstruct the final face sample. Then FLD is used to generate a supervised discriminant space for the classification task. Experiments show that the bimodal method outperforms conventional methods under complex conditions.

• （社）電子情報通信学会の論文
• 2009-02-01

著者

• RUAN Qiuqi

  Institute of Information Science, Beijing Jiaotong University

• Ruan Qiuqi

  Institute Of Information Science Beijing Jiaotong University

• WU JiYing

  Institute of Information Science, Beijing Jiaotong University

• AN Gaoyun

  Institute of Information Science, Beijing Jiaotong University

• Wu Jiying

  Institute Of Information Science Beijing Jiaotong University

• An Gaoyun

  Institute Of Information Science Beijing Jiaotong University

関連論文

• Neighbor-Aided Authentication Watermarking Based on a Chaotic System with Feedback
• Dual Two-Dimensional Fuzzy Class Preserving Projections for Facial Expression Recognition
• An Illumination Invariant Bimodal Method Employing Discriminant Features for Face Recognition
• Kernel TV-Based Quotient Image Employing Gabor Analysis and Its Application to Face Recognition
• Facial Expression Recognition via Sparse Representation

スポンサーリンク