Eyebrow shape-based features for biometric recognition and gender classification: A feasibility study

  • Authors:

  • Yujie Dong;Damon L. Woodard

  • Affiliations:

  • Department of Electrical and Computer Engineering, Clemson University, USA;Biometrics and Pattern Recognition Lab, School of Computing, Clemson University, USA

  • Venue:
  • IJCB '11 Proceedings of the 2011 International Joint Conference on Biometrics
  • Year:
  • 2011

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Abstract

A wide variety of applications in forensic, government, and commercial fields require reliable personal identification. However, the recognition performance is severely affected when encountering non-ideal images caused by motion blur, poor contrast, various expressions, or illumination artifacts. In this paper, we investigated the use of shape-based eyebrow features under non-ideal imaging conditions for biometric recognition and gender classification. We extracted various shape-based features from the eyebrow images and compared three different classification methods: Minimum Distance Classifier (MD), Linear Discriminant Analysis Classifier (LDA) and Support Vector Machine Classifier (SVM). The methods were tested on images from two publicly available facial image databases: The Multiple Biometric Grand Challenge (MBGC) database and the Face Recognition Grand Challenge (FRGC) database. Obtained recognition rates of 90% using the MBGC database and 75% using the FRGC database as well as gender classification recognition rates of 96% and 97% for each database respectively, suggests the shape-based eyebrow features maybe be used for biometric recognition and soft biometric classification.