Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Adini, Y., Moses, Y. and Ullman, S.: Face Recognition: The Problem of Compensating for Changes in Illumination Direction. IEEE Trans. on Pattern Analysis and Machine Intelligence 19 (1997), no. 7, 721–732.
Beveridge, R.: Face Recognition Vendor Test 2006 Experiment 4 Covariate Study. Presentation at the NIST MBGC Kick-off Workshop (2008).
Canon: Optical Terminology. The EF LensWork III, Canon Inc., Lens Products Group, 2006, 192–216.
Chen, X., Flynn, P.J. and Bowyer, K.W.: IR and Visible Light Face Recognition. Computer Image and Vision Understanding 99 (2005), no. 3, 332–358.
Chen, T., Yin,W., Zhou, X., Comaniciu, D. and Huang T.: Total Variation Models for Variable Lighting Face Recognition. IEEE Trans. Pattern Analysis Machine Intelligence 28 (2006), no. 9, 1519–1524.
Dou, M.S., Zhang, C., Hao, P.W. and Li, J.: Converting Thermal Infrared Face Images into Normal Gray-Level Images. The 2007 Asian Conference on Computer Vision, 2007, II: 722–732.
Flynn, P.: ICE Mining: Quality and Demographic Investigations of ICE 2006 Performance Results. Presentation at the NIST MBGC Kick-off Workshop (2008).
Georghiades, A.S., Kriegman, D.J. and Belhumeur, P.N.: Illumination Cones for Recognition under Variable Lighting: Faces. Proc. of 1998 IEEE Conf. on Computer Vision and Pattern Recognition, 1998, 52–58.
Hoist, G.C.: CCD Array, Cameras, and Displays. SPIE Optical Engineering, Bellingham 1996.
Jacobs, D.W., Belhumeur, P.N. and Basri, R.: Comparing Images Under Variable Illumination. Proc. of 1998 IEEE Conf. on Computer Vision and Pattern Recognition, 1998, 610–617.
Kong, S.G., Heo, J., Abidi, B.R., Paik, J.K. and Abidi, M.A.: Recent Advances in Visual and Infrared Face Recognition: A Review. Computer Vision and Image Understanding 97 (2005), no. 1, 103-135.
Li, W., Gao, X. and Boult, T.: Predicting Biometric System Failure. Proc. of the IEEE Conference on Computational Intelligence for Homeland Security and Personal Safety (CIHSPS 2005), 2005.
Micheals, R. and Boult, T.: Efficient evaluation of classification and recognition systems. Proc. of 2001 IEEE Conf. on Computer Vision and Pattern Recognition, 2001, I: 50:57.
Marasco, P. and Task, H.: The Impact of Target Luminance and Radiance on Night Vision Device Visual Performance Testing. Helmet- and Head-Mounted Displays VIII: Technologies and Applications. Edited by Rash, Clarence E.; Reese, Colin E. Proceedings of the SPIE, 5079 (2003), 174–183 .
Narasimhan, S. and Nayar, S.: Contrast Restoration of Weather Degraded Images. IEEE Trans. on Pattern Analysis and Machine Intelligence 25 (2003), no. 6, 713–724.
Phillips, P.J., Grother, P., Micheals, R., Blackburn, D., Tabassi, E. and Bone, M.: Face Recognition Vendor Test 2002 (FRVT 2002). National Institute of Standards and Technology, NISTIR 6965, 2003.
Phillips, P.J. and Vardi, Y.: Efficient Illumination Normalization of Facial Images. Pattern Recognition Letters 17 (1996), no. 8, 921–927.
Riopka, T. and Boult, T.: The Eyes Have It. ACM Biometrics Methods and Applications Workshop, 2003, 33–40.
Riopka, T. and Boult, T.: Classification Enhancement via Biometric Pattern Perturbation. IAPR Conference on Audio- and Video-based Biometric Person Authentication (Springer Lecture Notes in Computer Science) 3546 (2005), 850–859.
Scheirer, W. and Boult, T.: A Fusion Based Approach to Enhancing Multi-Modal Biometric Recognition System Failure and Overall Performance. In Proc. of the Second IEEE Conference on Biometrics: Theory, Applications, and Systems, 2008.
Scheirer, W., Bendale, A. and Boult, T.: Predicting Biometric Facial Recognition Failure With Similarity Surfaces and Support Vector Machines. In Proc. of the IEEE Computer Society Workshop on Biometrics, 2008.
Socolinsky, D., Wolff, L. and Lundberg, A.: Image Intensification for Low-Light Face Recognition. In Proc. of the IEEE Computer Society Workshop on Biometrics, 2006.
Socolinsky, D., Wolff, L., Neuheisel, J. and Eveland, C.: Illumination Invariant Face Recognition Using Thermal Infrared Imagery. Proc. of 2001 IEEE Conf. on Computer Vision and Pattern Recognition, 2001, I: 527:534.
Vogelsong, T., Boult, T., Gardner, D., Woodworth, R., Johnson, R. C. and Heflin, B.: 24/7 Security System: 60-FPS Color EMCCD Camera With Integral Human Recognition. Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense VI. Edited by Carapezza, Edward M. Proceedings of the SPIE 6538 (2007), 65381S.
Wilder, J., Phillips, P.J., Jiang, C. and Wiener, S.: Comparison of Visible and Infra-red Imagery for Face Recognition. Proc. of the IEEE Conf. on Automated Face and Gesture Recognition, 1996, 182–187.
Xie, B., Boult, T., Ramesh, V. and Zhu, Y.: Multi-Camera Face Recognition by Reliability-Based Selection. Proc. of the IEEE Conference on Computational Intelligence for Homeland Security and Personal Safety (CIHSPS 2006), 2006.
Yitzhaky, Y., Dror, I. and Kopeika, N.: Restoration of Atmospherically Blurred Images According to Weather Predicted Atmospheric Modulation Transfer Function (MTF). Optical Engineering 36 (1997), no. 11.
Zhang, Z. and Blum, R.: On Estimating the Quality of Noisy Images. Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing, 1998, 2897–2900.
Zhao, W. and Chellappa, R.: Illumination-Insensitive Face Recognition using Symmetric Shape-from-Shading. Proc. of 2000 IEEE Conf. on Computer Vision and Pattern Recognition, 2000, I: 286–293.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag London Limited
About this chapter
Cite this chapter
Boult, T.E., Scheirer, W. (2009). Long-Range Facial Image Acquisition and Quality. In: Tistarelli, M., Li, S.Z., Chellappa, R. (eds) Handbook of Remote Biometrics. Advances in Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-84882-385-3_7
Download citation
DOI: https://doi.org/10.1007/978-1-84882-385-3_7
Publisher Name: Springer, London
Print ISBN: 978-1-84882-384-6
Online ISBN: 978-1-84882-385-3
eBook Packages: Computer ScienceComputer Science (R0)