Earticle

초록

영어

This paper investigates the possibility of exploiting facial skin texture as a source of biometric information to facilitate automatic recognition of individuals. Such ability may be particularly important in circumstances when a full view of the face may not be available. The proposed algorithm automatically segments the forehead region and divides it into non-overlapping patches. Two state-of-the-art families of texture feature extraction approaches, namely Gabor wavelet filter and Local Binary Pattern operator, are compared for extracting features from these patches which are classified using a k-NN classifier. The identification and verification performance is evaluated for different patch sizes using the XM2VTS database. For the verification experiments an EER of 0.065 using Gabor features and 0.083 using LBP features is obtained for forehead regions with pure skin. Additionally a novel classifier is presented for automatically detecting pure skin patches in the forehead region.

목차

Abstract
 1. Introduction
 2. Recognition Based on Local Skin Patches
  2.1 Face Normalization
  2.2. Forehead Localization
  2.3. Forehead Partitioning and Feature Extraction
  2.4 Gabor Filter for Extracting Skin Texture Features
  2.5 Local Binary Pattern (LBP) Operator for Extracting Skin Texture Features
  2.6 Forehead Representation and Classification
 3. Developing Hair/Skin Patch Classifier
 4. Experimental Results
  4.1 Experiment 1: Identification Scenario
  4.2. Experiment 2: Verification Scenario
  4.3. Test of the Proposed Skin/Hair Classifier
 5. Conclusion and Future Work
 References

키워드

저자

• Garsah Farhan Al-Qarni [ School of Engineering and Digital Arts, University of Kent ]
• Farzin Deravi [ School of Engineering and Digital Arts, University of Kent ]

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