Long-Range Facial Image Acquisition and Quality

This chapter introduces issues in long-range facial image acquisition and measures for image quality and their usage. Section 7.1 on image acquisition for face recognition discusses issues in lighting, sensor, lens, blur issues, which impact shortrange biometrics but are more pronounced in long-range biometrics. Section 7.2 introduces the design of controlled experiments for long-range face and why they are needed. Section 7.3 introduces some of the weather and atmospheric effects that occur for long-range imaging, with numerous of examples. Section 7.4 addresses measurements of “system quality,” including image-quality measures and their use in prediction of face recognition algorithm. This section also introduces the concept of failure prediction and techniques for analyzing different “quality” measures. The section ends with a discussion of post-recognition “failure prediction” and its potential role as a feedback mechanism in acquisition. Each section includes a collection of open-ended questions to challenge the reader to think about the concepts more deeply. For some of the questions we answer them after they are introduced; others are left as an exercise for the reader.

[1]  Edward M. Carapezza,et al.  Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense XI , 2004 .

[2]  Peter L. Marasco,et al.  The impact of target luminance and radiance on night vision device visual performance testing , 2003, SPIE Defense + Commercial Sensing.

[3]  Terrance E. Boult,et al.  24/7 security system: 60-FPS color EMCCD camera with integral human recognition , 2007, SPIE Defense + Commercial Sensing.

[4]  Seong G. Kong,et al.  Recent advances in visual and infrared face recognition - a review , 2005, Comput. Vis. Image Underst..

[5]  Andrew J. Lundberg,et al.  Image Intensification for Low-Light Face Recognition , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[6]  Ronen Basri,et al.  Comparing images under variable illumination , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[7]  Shaogang Gong,et al.  Audio- and Video-based Biometric Person Authentication , 1997, Lecture Notes in Computer Science.

[8]  Joseph Wilder,et al.  Comparison of visible and infra-red imagery for face recognition , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[9]  Terrance E. Boult,et al.  Efficient evaluation of classification and recognition systems , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[10]  P. Jonathon Phillips,et al.  Efficient illumination normalization of facial image , 1996, Pattern Recognit. Lett..

[11]  Ying Zhu,et al.  Multi-Camera Face Recognition by Reliability-Based Selection , 2006, 2006 IEEE International Conference on Computational Intelligence for Homeland Security and Personal Safety.

[12]  Shimon Ullman,et al.  Face Recognition: The Problem of Compensating for Changes in Illumination Direction , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Chao Zhang,et al.  Converting Thermal Infrared Face Images into Normal Gray-Level Images , 2007, ACCV.

[14]  Gerald C. Holst,et al.  CCD arrays, cameras, and displays , 1996 .

[15]  Rick S. Blum,et al.  On estimating the quality of noisy images , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[16]  Xin Chen,et al.  IR and visible light face recognition , 2005, Comput. Vis. Image Underst..

[17]  Carl Eklund,et al.  National Institute for Standards and Technology , 2009, Encyclopedia of Biometrics.

[18]  Xiang Gao,et al.  Predicting biometric system failure , 2005, CIHSPS 2005. Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Homeland Security and Personal Safety, 2005..

[19]  Terrance E. Boult,et al.  Classification Enhancement via Biometric Pattern Perturbation , 2005, AVBPA.

[20]  I. Dror,et al.  RESTORATION OF ATMOSPHERICALLY BLURRED IMAGES ACCORDING TO WEATHER-PREDICTED ATMOSPHERIC MODULATION TRANSFER FUNCTIONS , 1997 .

[21]  Rama Chellappa,et al.  Illumination-insensitive face recognition using symmetric shape-from-shading , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[22]  Lawrence B. Wolff,et al.  Illumination invariant face recognition using thermal infrared imagery , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[23]  T.E. Boult,et al.  A Fusion-Based Approach to Enhancing Multi-Modal Biometric Recognition System Failure Prediction and Overall Performance , 2008, 2008 IEEE Second International Conference on Biometrics: Theory, Applications and Systems.

[24]  Terrance E. Boult,et al.  Predicting biometric facial recognition failure with similarity surfaces and support vector machines , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[25]  David J. Kriegman,et al.  Illumination cones for recognition under variable lighting: faces , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[26]  Dorin Comaniciu,et al.  Total variation models for variable lighting face recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Shree K. Nayar,et al.  Contrast Restoration of Weather Degraded Images , 2003, IEEE Trans. Pattern Anal. Mach. Intell..