Measuring measures for face sample quality

The quality of input samples is a crucial issue for both verification and identification biometric systems. Literature offers many interesting hints in some contexts, like fingerprint matching, but it is still deficient in others, like face matching. This paper proposes new quality indices for face samples which are based either on the measure of pose or illumination distortion, or on the measure of face symmetry. Results show that they give better results than other proposals in literature.

[1]  Harry Wechsler,et al.  The FERET database and evaluation procedure for face-recognition algorithms , 1998, Image Vis. Comput..

[2]  Bruce A. Draper,et al.  Factors that influence algorithm performance in the Face Recognition Grand Challenge , 2009, Comput. Vis. Image Underst..

[3]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[4]  Sabah Jassim,et al.  Image-Quality-Based Adaptive Face Recognition , 2010, IEEE Transactions on Instrumentation and Measurement.

[5]  Fred Nicolls,et al.  Locating Facial Features with an Extended Active Shape Model , 2008, ECCV.

[6]  Arun Ross,et al.  Score normalization in multimodal biometric systems , 2005, Pattern Recognit..

[7]  Enrique G. Ortiz,et al.  Evaluation of face recognition techniques for application to facebook , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[8]  Mislav Grgic,et al.  SCface – surveillance cameras face database , 2011, Multimedia Tools and Applications.

[9]  Douglas A. Reynolds,et al.  SHEEP, GOATS, LAMBS and WOLVES A Statistical Analysis of Speaker Performance in the NIST 1998 Speaker Recognition Evaluation , 1998 .

[10]  Takeo Kanade,et al.  Neural Network-Based Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Andrea F. Abate,et al.  2D and 3D face recognition: A survey , 2007, Pattern Recognit. Lett..

[12]  Jian-Jun Zhang,et al.  Self quotient image for face recognition , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..

[13]  A. Bovik,et al.  A universal image quality index , 2002, IEEE Signal Processing Letters.

[14]  J. Fierrez-Aguilar,et al.  On the effects of image quality degradation on minutiae- and ridge-based automatic fingerprint recognition , 2022, Proceedings 39th Annual 2005 International Carnahan Conference on Security Technology.

[15]  Akshay Chandra Dey,et al.  Worms , 1919, The Indian medical gazette.

[16]  Bruce A. Draper,et al.  Quantifying how lighting and focus affect face recognition performance , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[17]  Neil Yager,et al.  Worms, Chameleons, Phantoms and Doves: New Additions to the Biometric Menagerie , 2007, 2007 IEEE Workshop on Automatic Identification Advanced Technologies.

[18]  Xiaoming Liu,et al.  Improving face recognition with a quality-based probabilistic framework , 2009, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[19]  Marwan Mattar,et al.  Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments , 2008 .

[20]  Elham Tabassi,et al.  Performance of Biometric Quality Measures , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Michele Nappi,et al.  Face: face analysis for Commercial Entities , 2010, 2010 IEEE International Conference on Image Processing.

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

[23]  Krzysztof Kryszczuk,et al.  Reliability-Based Decision Fusion in Multimodal Biometric Verification Systems , 2007, EURASIP J. Adv. Signal Process..

[24]  S. Buxbaum.  Sheep , 2004 .

[25]  Yanxi Liu,et al.  Facial Asymmetry: A New Biometric , 2001 .

[26]  Jiawei Han,et al.  Orthogonal Laplacianfaces for Face Recognition , 2006, IEEE Transactions on Image Processing.