Kernel TV-Based Quotient Image Employing Gabor Analysis and Its Application to Face Recognition

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概要

• 論文の詳細を見る

• In order to overcome the drawback of TVQI and to utilize the property of dimensionality increasing techniques, a novel model for Kernel TV-based Quotient Image employing Gabor analysis is proposed and applied to face recognition with only one sample per subject. To deal with illumination outliers, an enhanced TV-based quotient image (ETVQI) model is first adopted. Then for preprocessed images by ETVQI, a bank of Gabor filters is built to extract features at specified scales and orientations. Lastly, KPCA is introduced to extract final high-order and nonlinear features of extracted Gabor features. According to experiments on the CAS-PEAL face database, our model could outperform Gabor-based KPCA, TVQI and Gabor-based TVQI when they face most outliers (illumination, expression, masking etc.).

• （社）電子情報通信学会の論文
• 2008-05-01

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

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• 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

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