Complex kernel PCA for multimodal biometric recognition

スポンサーリンク

概要

• 論文の詳細を見る

• This letter presents a novel multimodal biometric recognition algorithm based on complex kernel principle component analysis (CKPCA). CKPCA generalizes kernel principle component analysis (KPCA) method for complex field to perform feature fusion and classification. Iris and face are used as two distinct biometric modals to test our algorithm. Experimental results show that the proposed algorithm achieves much better performance than other conventional multimodal biometric algorithms.

• The Institute of Electronics, Information and Communication Engineersの論文

著者

• Niu Xiamu

  School Of Computer Science And Technology Harbin Institute Of Technology

• Han Qi

  School of Computer Science and Technology, Harbin Institute of Technology

• Wang Zhifang

  School of Computer Science and Technology, Harbin Institute of Technology

関連論文

• 3D Mesh Segmentation Based on Markov Random Fields and Graph Cuts
• A Multi-Scale Structural Degradation Metric for Perceptual Evaluation of 3D Mesh Simplification
• A Novel Expression Deformation Model for 3D Face Recognition
• Complex kernel PCA for multimodal biometric recognition

スポンサーリンク