Skip to main content
SpringerLink
Log in

Remote Identification of Faces

  • Chapter
  • First Online:
Signal and Image Processing for Biometrics

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 292))

Abstract

Face recognition in unconstrained acquisition conditions is one of the most challenging problems that has been actively researched in recent years. It is well known that many state-of-the-art still image-based face recognition algorithms perform well, when constrained (frontal, well illuminated, high-resolution, sharp, and full) face images are acquired. However, their performance degrades significantly when the test images contain variations that are not present in the training images. In this chapter, we highlight some of the key issues in remote face recognition. We define the remote face recognition as one where faces are several tens of meters (10–250 m) from the cameras. We then describe a remote face database which has been acquired in an unconstrained outdoor maritime environment. The face images in this database suffer from variations due to blur, poor illumination, pose, etc. Recognition performance of a subset of existing still image-based face recognition algorithms is discussed on the remote face data set. It is demonstrated that in addition to applying a good classification algorithm, finding features that are robust to variations mentioned above and developing statistical models which can account for these variations are very important for remote face recognition.

This work was partially supported by a MURI grant N00014-08-1-0638 from the Office of Naval Research.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abiantun R, Savvides M, Vijaya Kumar BVK (2006)How low can you go? low resolution face recognition study using kernel correlation feature analysis on the frgcv2 dataset. In: Biometric consortium conference, 2006 biometrics symposium: special session on research at the, pp 1–6

    Google Scholar 

  2. Ahonen T, Rahtu E, Ojansivu V, Heikkila J (2008) Recognition of blurred faces using local phase quantization. In: International conference on pattern recognition, pp 1–4

    Google Scholar 

  3. Arandjelovic OD, Cipolla R (2006) Face recognition from video using the generic shape-illumination manifold. In: European conference on computer vision, vol IV, pp 27–40

    Google Scholar 

  4. Arashloo SR, Kittler J (2011) Energy normalization for pose-invariant face recognition based on mrf model image matching. IEEE Trans Pattern Anal Mach Intell 33(6):1274–1280

    Article  Google Scholar 

  5. Ashraf AB, Lucey S, Chen T (2008) Learning patch correspondences for improved viewpoint invariant face recognition. In: IEEE conference on computer vision and pattern recognition, pp 1–8

    Google Scholar 

  6. Bartlett MS, Movellan JR, Sejnowski TJ (2002) Face recognition by independent component analysis. IEEE Trans Neural Networks 13(6):1450–1464

    Article  Google Scholar 

  7. Basri R, Jacobs DW (2003) Lambertian reflectance and linear subspaces. IEEE Trans Pattern Anal Mach Intell 25(2):218–233

    Article  Google Scholar 

  8. Belhumeur P, Hespanda J, Kriegman D (1997) Eigenfaces versus fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720

    Article  Google Scholar 

  9. Belhumeur PN, Kriegman DJ (1996) What is the set of images of an object under all possible lighting conditions? In: IEEE conference on computer vision and pattern recognition

    Google Scholar 

  10. Biswas S, Aggarwal G, Chellappa R (2009) Robust estimation of albedo for illumination-invariant matching and shape recovery. IEEE Trans Pattern Anal Mach Intell 29(2):884–899

    Article  Google Scholar 

  11. Biswas S, Bowyer KW, Flynn PJ (2010) Multidimensional scaling for matching low-resolution facial images. In: IEEE international conference on biometrics: theory applications and systems, pp 1–6

    Google Scholar 

  12. Biswas S, Chellappa R (2010) Pose-robust albedo estimation from a single image. In: IEEE conference on computer vision and pattern recognition, pp 2683–2690

    Google Scholar 

  13. Blanz Volker, Vetter Thomas (2003) Face recognition based on fitting a 3d morphable model. IEEE Trans Pattern Anal Mach Intell 25:1063–1074

    Article  Google Scholar 

  14. Candès EJ, Li X, Ma Y, Wright J (2011) Robust principal component analysis? J ACM 58(1): 1–37

    Google Scholar 

  15. Castillo CD Jacobs DW (2009) Using stereo matching with general epipolar geometry for 2d face recognition across pose. IEEE Trans Pattern Anal Mach Intell 31(12):2298–2304

    Google Scholar 

  16. Chai Xiujuan, Shan Shiguang, Chen Xilin, Gao Wen (2007) Locally linear regression for pose-invariant face recognition. IEEE Trans Image Process 16(7):1716–1725

    Article  MathSciNet  Google Scholar 

  17. Chellappa R, Ni J, Patel VM (2012) Remote identification of faces: problems, prospects, and progress. Pattern Recogn Lett 33(15):1849–1859

    Google Scholar 

  18. Chen T, Yin W, Zhou XS, Comaniciu D, Huang TS (2006) Total variation models for variable lighting face recognition. IEEE Trans Pattern Anal Mach Intell 28(9):1519–1524

    Article  Google Scholar 

  19. Chen Y-C, Patel VM, Phillips PJ, Chellappa R (2012) Dictionary-based face recognition from video. In: European conference on computer vision

    Google Scholar 

  20. Etemad K, Chellappa R (1997) Discriminant analysis for recognition of human face images. J Opt Soc Am 14:1724–1733

    Google Scholar 

  21. Friedman J (1989) Regularized discriminant analysis. J Am Stat Assoc 84:165–175

    Article  Google Scholar 

  22. Georghiades AS, Belhumeur PN, Kriegman DJ (2001) From few to many: ilumination cone models for face recognition under variable lighting and pose. IEEE Trans Pattern Anal Mach Intell 23(6):643–660

    Article  Google Scholar 

  23. Georghiades AS, Belhumeur PN, Kriegman DJ (2001) From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans Pattern Anal Mach Intell 23(6):643–660

    Article  Google Scholar 

  24. Gross R, Matthews I, Baker S (2004) Appearance-based face recognition and light-fields. IEEE Trans Pattern Anal Mach Intell 26(4):449–465

    Article  Google Scholar 

  25. Gunturk BK, Batur AU, Altunbasak Y, Hayes III MH, Mersereau RM (2003) Eigenface-domain super-resolution for face recognition. IEEE Trans Image Process 12(5):597–606

    Google Scholar 

  26. Guo G, Li SZ, Chan K (2000) Face recognition by support vector machines. In: IEEE international conference on automatic face and gesture recognition, Grenoble, France, pp 196–201

    Google Scholar 

  27. Hennings-Yeomans PH, Baker S, Kumar BVKV (2008) Simultaneous super-resolution and feature extraction for recognition of low-resolution faces. In: IEEE international conference on computer vision and pattern recognition, pp 1–8

    Google Scholar 

  28. Hotta K, Kurita T, Mishima T (1998) Scale invariant face detection method using higher-order local autocorrelation features extracted from log-polar image. In: IEEE international conference on automatic face and gesture recognition, pp 70–75

    Google Scholar 

  29. Huang G, Ramesh M, Berg T, Learned-Miller E (2007) Labeled faces in the wild: a database for studying face recognition in unconstrained environments. University of Massachusetts, Amherst, Technical Report , pp 07–49

    Google Scholar 

  30. Jia K, Gong S (2005) Multi-modal tensor face for simultaneous super-resolution and recognition. In: IEEE international conference on computer vision, vol 2, pp 1683–1690

    Google Scholar 

  31. Kanade T, Yamada A (2003) Multi-subregion based probabilistic approach toward pose-invariant face recognition. In: IEEE international symposium on computational intelligence in robotics and automation, vol 2, pp 954–959

    Google Scholar 

  32. Lee K, Ho J, Kriegman DJ (2005) Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans Pattern Anal Mach Intell 27(5):684–698

    Article  Google Scholar 

  33. Lee K-C, Ho J, Yang M-H, Kriegman D (2005) Visual tracking and recognition using probabilistic appearance manifolds. Comput Vis Image Underst 99:303–331

    Google Scholar 

  34. Lee Sang-Woong, Park Jooyoung, Lee Seong-Whan (2006) Low resolution face recognition based on support vector data description. Pattern Recognit 39(9):1809–1812

    Article  MATH  Google Scholar 

  35. Li A, Shan S, Chen X, Gao W (2009) Maximizing intra-individual correlations for face recognition across pose differences. In: IEEE conference on computer vision and pattern recognition, pp 605–611

    Google Scholar 

  36. Li B, Chang H, Shan S, Chen X (2009) Coupled metric learning for face recognition with degraded images. In: Asian conference on machine learning: advances in machine learning, Berlin, Heidelberg, Springer, pp 220–233

    Google Scholar 

  37. Li B, Chang H, Shan S, Chen X (2010) Low-resolution face recognition via coupled locality preserving mappings. IEEE Signal Process Lett 17(1):20–23

    Google Scholar 

  38. Medioni G, Choi J, Kuo C-H, Choudhury A, Zhang L, Fidaleo D (2007) Non-cooperative persons identification at a distance with 3d face modeling. In: IEEE international conference on biometrics: theory, paplications, and systems, pp 1–6

    Google Scholar 

  39. Moghaddam B (2000) Bayesian face recognition. Pattern Recognit 33:1771–1782

    Article  Google Scholar 

  40. Narasimhan SG, Nayar SK (2003) Shedding light on the weather. In: IEEE conference on computer vision and pattern recognition, vol 1, pp I-665–I-672

    Google Scholar 

  41. Nayar SK, Narasimhan SG (1999) Vision in bad weather. In: IEEE international conference on computer vision, vol 2, pp 820–827

    Google Scholar 

  42. Ni J, Turaga P, Patel VM, Chellappa R (2011) Example-driven manifold priors for image deconvolution. IEEE Trans Image Process 20(11):3086–3096

    Google Scholar 

  43. Nishiyama M, Hadid A, Takeshima H, Shotton J, Kozakaya T, Yamaguchi O (2011) Facial deblur inference using subspace analysis for recognition of blurred faces. IEEE Trans Pattern Anal Mach Intell 33(4):838–845

    Article  Google Scholar 

  44. Patel VM, Wu T, Biswas S, Philips PJ, Chellappa R (2012) Dictionary-based face recognition under variable lighting and pose. IEEE Trans Inf Forensics Secur 7(3):954–965

    Google Scholar 

  45. Patel VM, Wu T, Biswas S, Phillips PJ, Chellappa R (2011) Illumination robust dictionary-based face recognition. In: International conference on image processing

    Google Scholar 

  46. Phillips PJ, Wechsler H, Huang J, Rauss PJ (1998) The feret database and evaluation procedure for face-recognition algorithms. Image Vis Comput 16:295–306

    Article  Google Scholar 

  47. Pinto N, DiCarlo J, Cox D (2009) How far can you get with a modern face recognition test set using only simple features? In: IEEE conference on computer vision and pattern recognition, Miami, FL, pp 2591–2568

    Google Scholar 

  48. Prince SJD, Warrell J, Elder JH, Felisberti FM (2008) Tied factor analysis for face recognition across large pose differences. IEEE Trans Pattern Anal Mach Intell 30(6):970–984

    Article  Google Scholar 

  49. Ramamoorthi R, Hanrahan P (2001) On the relationship between radiance and irradiance: determining the illumination from images of a convex lambertian object. J Opt Soc Am 18(10):2448–2459

    Google Scholar 

  50. Rara HM, Elhabian SY, Ali AM, Miller M, Starr TL, Farag AA (2009) Distant face recognition based on sparse-stereo reconstruction. In: IEEE international conference on image processing, pp 4141–4144

    Google Scholar 

  51. Shashua A, Riklin-Raviv T (2001) The quotient image: class-based re-rendering and recognition with varying iluminations. IEEE Trans Pattern Anal Mach Intell 23(2):129–139

    Article  Google Scholar 

  52. Shekhar S, Patel VM, Chellappa R (2011) Synthesis-based recognition of low resolution faces, International Joint Conference on Biometrics (IJCB) 1–6. Washington, DC

    Google Scholar 

  53. Sim T, Baker S, Bsat M (2003) The cmu pose, illumination, and expression database. IEEE Trans Pattern Anal Mach Intell 25:1615–1618

    Article  Google Scholar 

  54. Taheri S, Sankaranarayanan AC, Chellappa R (2012) Joint albedo estimation and pose tracking from video. IEEE Trans Pattern Anal Mach Intell 35(7):1674–1689

    Google Scholar 

  55. Tistarelli M, Li SZ, Chellappa R (2009) Handbook of remote biometrics: for surveillance and security, 1st edn. Springer Publishing Company Incorporated, New York

    Google Scholar 

  56. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3:71–86

    Google Scholar 

  57. Wang H, Li SZ, Wang Y (2004) Generalized quotient image. In: International conference on computer vision and pattern recognition

    Google Scholar 

  58. Wiskott Laurenz, Fellous Jean-Marc, Krger Norbert, Von Der Malsburg Christoph (1997) Face recognition by elastic bunch graph matching. IEEE Trans Pattern Anal Mach Intell 19:775–779

    Article  Google Scholar 

  59. Wright J, Ganesh A, Yang A, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31:210–227

    Article  Google Scholar 

  60. Yang M-H (2002) Kernel eigenfaces vs. kernel fisherfaces: face recognition using kernel methods. In: IEEE international conference on automatic face and gesture recognition, Washington, DC, pp 215–220

    Google Scholar 

  61. Yao Y, Abidi B, Kalka N, Schmid N, Abidi M (2008) Improving long range and high magnification face recognition: database acquisition, evaluation, and enhancement. Comput Vis Image Underst 111:111–125

    Google Scholar 

  62. Zhang L, Samaras D (2003) Face recognition under variable lighting using harmonic image exemplars. In: International conference on computer vision and pattern recognition

    Google Scholar 

  63. Zhang T, Tang YY, Fang B, Shang Z, Liu X (2009) Face recognition under varying illumination using gradientfaces. IEEE Trans Image Process 18(11):2599–2606

    Article  MathSciNet  Google Scholar 

  64. Zhang Xiaozheng, Gao Yongsheng (2009) Face recognition across pose: a review. Pattern Recogn 42:2876–2896

    Article  Google Scholar 

  65. Zhang Z, Blum RS (1998) On estimating the quality of noisy images. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 5:2897–2900. Seattle, Washington

    Google Scholar 

  66. Zhao W, Chellappa R, Phillips J, Rosenfeld A (2003) Face recognition: a literature survey. ACM Comput Surv 35(4):399–458

    Google Scholar 

  67. Zhou SK, Aggarwal G, Chellappa R, Jacobs DW (2007) Appearance characterization of linear lambertian objects, generalized photometric stereo, and illumination-invariant face recognition. IEEE Trans Pattern Anal Mach Intell 29(2):230–245

    Article  Google Scholar 

  68. Zhou S, Krueger V, Chellappa R (2003) Probabilistic recognition of human faces from video. Comput Vis Image Underst 91:214–245

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Center for Automation Research, University of Maryland, College Park, MD, 20742, USA

    Vishal M. Patel, Jie Ni & Rama Chellappa

Authors
  1. Vishal M. Patel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jie Ni
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rama Chellappa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vishal M. Patel .

Editor information

Editors and Affiliations

  1. Federal University of Rio Grande do Sul, Porto Alegre, Brazil

    Jacob Scharcanski

  2. IT-Instituto de Telecomunicações, University of Beira Interior, Covilhã, Portugal

    Hugo Proença

  3. Indiana University-Purdue University Indianapolis, Indianapolis, Indiana, USA

    Eliza Du

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Patel, V.M., Ni, J., Chellappa, R. (2014). Remote Identification of Faces. In: Scharcanski, J., Proença, H., Du, E. (eds) Signal and Image Processing for Biometrics. Lecture Notes in Electrical Engineering, vol 292. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54080-6_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-54080-6_2

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-54079-0

  • Online ISBN: 978-3-642-54080-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation