Standardization of Face Image Sample Quality

Performance of biometric systems is dependent on quality of acquired biometric samples. Poor sample quality is a main reason for matching errors in biometric systems and may be the main weakness of some implementations. In this paper, we present an approach for standardization of facial image quality, and develop facial symmetry based methods for its assessment by which facial asymmetries caused by non-frontal lighting and improper facial pose can be measured. Experimental results are provided to illustrate the concepts, definitions and effectiveness.

[1]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[2]  Anil K. Jain,et al.  Localized Iris Image Quality Using 2-D Wavelets , 2006, ICB.

[3]  J. Meigs,et al.  WHO Technical Report , 1954, The Yale Journal of Biology and Medicine.

[4]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Xihong Wu,et al.  Boosting Local Binary Pattern (LBP)-Based Face Recognition , 2004, SINOBIOMETRICS.

[6]  David J. Kriegman,et al.  From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Shengcai Liao,et al.  Illumination Invariant Face Recognition Using Near-Infrared Images , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Elham Tabassi,et al.  Fingerprint Image Quality , 2009, Encyclopedia of Biometrics.

[9]  R. Youmaran,et al.  Measuring Biometric Sample Quality in Terms of Biometric Information , 2006, 2006 Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference.

[10]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..