Earticle

초록

영어

This paper addresses a probabilistic approach to develop a robust face recognition system to partial variations such as occlusions. Based on the statistical feature extraction methods, we take the modular PCA method which nds eigenspace not for the set of whole images but for the sets of local image patches. Through the local feature extraction approach, we try to overcome the drawback of wholistic appearance-based conventional PCA, and consequently expect to improve robustness to local variations. The obtained local features are then applied to dene two probabilistic models for facial images: one for modeling distribution of features observed in usual facial images, and the other for modeling distribution of environmental variations observed in face image from one subject. The probabilistic model for general fa- cial images are used to evaluate the importance of each local patch. The probabilistic model for the environmental variations is used to evaluate the similarity between two local fea- tures. By combining two probabilistic models, we nally dene a distance measure between two face images, which can be applied for face recognition. Computational experiments on benchmark face database show that the proposed face recognition method can achieve re- markable robustness to local variations.

목차

Abstract
 1: Introduction
 2: Probabilistic model for general facial images
 3: Probabilistic similarity measure for face recognition
 4: Experimental Comparisons
  4.1: Experimental data
  4.2: Experimental results
 5: Conclusions
 Acknowledgements
 References

키워드

저자

• Kwanyong Lee [ Korea National Open University, Seoul, Korea ]
• Hyeyoung Park [ School of Electrical Engineering and Computer Science Kyungpook National University, Daegu, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605