Earticle

초록

영어

The performance of ear recognition is influenced by pose variation. For the similar position of ear and profile face, a multimodal recognition method is proposed based on the feature fusion of ear and profile face information. A model for ear and profile face feature fusion and recognition is built. The Log-Gabor features of ear and profile face are first extracted separately, and two features are integrated into a combined feature after two Log-Gabor features are standardized. Then combined feature is mapped to kernel space to fuse further, and acquired stronger discriminant feature for classification by kernel Fisher discriminant analysis (KFDA). The minimum distance classifier is finally used in recognition. Experimental results on the profile face database of Notre Dame University show that the fused method improves the recognition rate of pose variation, and the performance of multimodal recognition is better than unimodal recognition using either ear or profile face alone. The method of ear and profile face feature fusion and recognition is effective and robust for the pose variation.

목차

Abstract
 1. Introduction
 2. Recognition Model of Ear and Profile Feature Fusion
  2.1. Log-Gabor Filters
  2.2. Ear and Profile Face Images Log-Gabor Filtering and Parameter Selection
  2.3. Feature Standardization and Combination
  2.4. Feature Fusion and Recognition using KFDA
 3. Experiments Results
 4. Conclusions
 References

키워드

저자

• Songze Lei [ School of Computer Science and Engineering, Xi’an Technological University, Xi’an 710032, China ]
• Min Qi [ School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605