Advertisement

Face Recognition Technologies for Evidential Evaluation of Video Traces

  • Xingjie Wei
  • Chang-Tsun Li
Chapter
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

Human recognition from video traces is an important task in forensic investigations and evidence evaluations. Compared with other biometric traits, face is one of the most popularly used modalities for human recognition due to the fact that its collection is non-intrusive and requires less cooperation from the subjects. Moreover, face images taken at a long distance can still provide reasonable resolution, while most biometric modalities, such as iris and fingerprint, do not have this merit. In this chapter, we discuss automatic face recognition technologies for evidential evaluations of video traces. We first introduce the general concepts in both forensic and automatic face recognition , then analyse the difficulties in face recognition from videos . We summarise and categorise the approaches for handling different uncontrollable factors in difficult recognition conditions. Finally we discuss some challenges and trends in face recognition research in both forensics and biometrics . Given its merits tested in many deployed systems and great potential in other emerging applications, considerable research and development efforts are expected to be devoted in face recognition in the near future.

Keywords

Face Recognition Face Image Illumination Change Equal Error Rate False Acceptance Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Abate AF, Nappi M, Riccio D, Sabatino G (2007) 2D and 3D face recognition: a survey. Pattern Recognit Lett 28(14):1885–1906. Image: Information and ControlGoogle Scholar
  2. 2.
    Ahonen T, Rahtu E, Ojansivu V, Heikkila J (2008) Recognition of blurred faces using local phase quantization. In: International conference on pattern recognition (ICPR), pp 1–4Google Scholar
  3. 3.
    Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12):2037–2041Google Scholar
  4. 4.
    Arandjelović O, Cipolla R (2007) A manifold approach to face recognition from low quality video across illumination and pose using implicit super-resolution. In: IEEE international conference computer vision (ICCV), pp 1–8Google Scholar
  5. 5.
    Arandjelović O, Cipolla R (2009) A pose-wise linear illumination manifold model for face recognition using video. Comput Vis Image Underst 113(1):113–125Google Scholar
  6. 6.
    Barr JR, Bowyer KW, Flynn PJ, Biswas S (2012) Face recognition from video: a review. Int J Pattern Recognit Artif Intell 26(5)Google Scholar
  7. 7.
    Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720Google Scholar
  8. 8.
    Belhumeur PN, Kriegman D (1996) What is the set of images of an object under all possible lighting conditions? In: IEEE conference computer vision and pattern recognition (CVPR), pp 270–277Google Scholar
  9. 9.
    Bhatt HS, Singh R, Vatsa M (2014) On recognizing faces in videos using clustering-based re-ranking and fusion. IEEE Trans Inf Forensics Secur 9(7):1056–1068CrossRefGoogle Scholar
  10. 10.
    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 (BTAS), pp 1–6Google Scholar
  11. 11.
    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):2298–2304CrossRefGoogle Scholar
  12. 12.
    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):1519–1524CrossRefGoogle Scholar
  13. 13.
    Chen YC, Patel VM, Phillips PJ, Chellappa R (2012) Dictionary-based face recognition from video. In: Fitzgibbon A, Lazebnik S, Perona P, Sato Y, Schmid C (eds) European conference computer vision (ECCV), vol 7577. Lecture notes in computer science. Springer, Berlin, pp 766–779Google Scholar
  14. 14.
    Du M, Sankaranarayanan AC, Chellappa R (2014) Robust face recognition from multi-view videos. IEEE Trans Image Process 23(3):1105–1117Google Scholar
  15. 15.
    Du S, Ward R (2005) Wavelet-based illumination normalization for face recognition. In: IEEE international conference on image processing (ICIP), vol 2, pp II-954-7Google Scholar
  16. 16.
    Firth N (2011) Face recognition technology fails to find UK rioters. New SciGoogle Scholar
  17. 17.
    Gabriel H, del Solar JR, Verschae R, Correa M (2012) A comparative study of thermal face recognition methods in unconstrained environments. Pattern Recognit 45(7):2445–2459Google Scholar
  18. 18.
    Gao Y, Leung MKH (2002) Face recognition using line edge map. IEEE Trans Pattern Anal Mach Intell 24(6):764–779Google Scholar
  19. 19.
    Geng X, Zhou Z-H, Smith-Miles K (2007) Automatic age estimation based on facial aging patterns. IEEE Trans Pattern Anal Mach Intell 29(12):2234–2240Google Scholar
  20. 20.
    Georghiades AS, Belhumeur PN, Kriegman D (2001) From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans Pattern Anal Mach Intell 23(6):643–660CrossRefGoogle Scholar
  21. 21.
    Guan Y, Wei X, Li C-T, Marcialis GL, Roli F, Tistarelli M (2013) Combining gait and face for tackling the elapsed time challenges. In: IEEE international conference on biometrics: theory, applications, and systems (BTAS), pp 1–8Google Scholar
  22. 22.
    Guodong G, Fu Y, Dyer CR, Huang TS (2008) Image-based human age estimation by manifold learning and locally adjusted robust regression. IEEE Tran Image Process 17(7):1178–1188Google Scholar
  23. 23.
    Hadid A, Nishiyama M, Sato Y (2010) Recognition of blurred faces via facial deblurring combined with blur-tolerant descriptors. In: 2010 20th international conference on pattern recognition (ICPR), pp 1160–1163Google Scholar
  24. 24.
    Hua F, Johnson P, Sazonova N, Lopez-Meyer P, Schuckers S (2012) Impact of out-of-focus blur on face recognition performance based on modular transfer function. In: IAPR international conference biometrics (ICB), pp 85–90Google Scholar
  25. 25.
    Jia H, Martínez AM (2008) Face recognition with occlusions in the training and testing sets. In: IEEE international conference automatic face and gesture recognition (FG), pp 1–6Google Scholar
  26. 26.
    Jia H, Martínez AM (2009) Support vector machines in face recognition with occlusions. In: IEEE conference computer vision and pattern recognition (CVPR), pp 136–141Google Scholar
  27. 27.
    Jiang D, Hu Y, Yan S, Zhang L, Zhang H, Gao W (2005) Efficient 3d reconstruction for face recognition. Pattern Recognit 38(6):787–798. Image Understanding for PhotographsGoogle Scholar
  28. 28.
    Kanade T (1973) Picture processing system by computer complex and recognition of human faces. In: Doctoral dissertation, Kyoto UniversityGoogle Scholar
  29. 29.
    Klare B, Jain AK (2010) Heterogeneous face recognition: matching NIR to visible light images. In: International conference on pattern recognition (ICPR), pp 1513–1516Google Scholar
  30. 30.
    Klontz JC, Jain AK (2013) A case study on unconstrained facial recognition using the Boston marathon bombings suspects. Technical Report MSU-CSE-13-4Google Scholar
  31. 31.
    Lambert J (1760) Photometria sive de mensura et gradibus luminus. Colorum et Umbrae, Eberhard KlettGoogle Scholar
  32. 32.
    Li B, Chellappa R (2002) A generic approach to simultaneous tracking and verification in video. IEEE Trans Image Process 11(5):530–544Google Scholar
  33. 33.
    Li B, Chang H, Shan S, Chen X (2010) Low-resolution face recognition via coupled locality preserving mappings. IEEE Signal Process Lett 17(1):20–23Google Scholar
  34. 34.
    Li SZ, Chu R, Liao S, Zhang L (2007) Illumination invariant face recognition using near-infrared images. IEEE Trans Pattern Anal Mach Intell 29(4):627–639Google Scholar
  35. 35.
    Li Y, Gong S, Sherrah J, Liddell H (2004) Support vector machine based multi-view face detection and recognition. Image Vis Comput 22(5):413–427Google Scholar
  36. 36.
    Liao S, Jain AK, Li SZ (2013) Partial face recognition: alignment-free approach. IEEE Trans Pattern Anal Mach Intell 35(5):1193–1205Google Scholar
  37. 37.
    Liu X, Chen I (2003) Video-based face recognition using adaptive hidden markov models. In: IEEE conference computer vision and pattern recognition (CVPR), vol 1, pp I–340–I–345Google Scholar
  38. 38.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110CrossRefGoogle Scholar
  39. 39.
    Martínez AM (2002) Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class. IEEE Trans Pattern Anal Mach Intell 24(6):748–763CrossRefGoogle Scholar
  40. 40.
    Meuwly D, Veldhuis R (2012) Forensic biometrics: from two communities to one discipline. In: International conference of the biometrics special interest group BIOSIG, Darmstadt, Germany, pp 1–12Google Scholar
  41. 41.
    Min R, Hadid A, Dugelay J-C (2011) Improving the recognition of faces occluded by facial accessories. In: IEEE international conference automatic face and gesture recognition (FG), pp 442–447Google Scholar
  42. 42.
    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):838–845CrossRefGoogle Scholar
  43. 43.
    Shekhar S, Patel VM, Chellappa R (2011) Synthesis-based recognition of low resolution faces. In: IEEE international joint conference on biometrics (IJCB), pp 1–6Google Scholar
  44. 44.
    Storer M, Urschler M, Bischof H (2010) Occlusion detection for ICAO compliant facial photographs. In: IEEE conference computer vision and pattern recognition workshops (CVPRW), pp 122–129Google Scholar
  45. 45.
    Tan X, Chen S, Zhou Z-H, Liu J (2009) Face recognition under occlusions and variant expressions with partial similarity. IEEE Trans Inf Forensics Secur 4(2):217–230Google Scholar
  46. 46.
    Travis A (2008) Police trying out national database with 750,000 mugshots. MPs told. The guardianGoogle Scholar
  47. 47.
    Turk MA, Pentland AP (1991) Face recognition using eigenfaces. In: IEEE conference computer vision and pattern recognition (CVPR), pp 586–591Google Scholar
  48. 48.
    Tzimiropoulos G, Zafeiriou S, Pantic M (2012) Subspace learning from image gradient orientations. IEEE Trans Pattern Anal Mach Intell 34(12):2454–2466Google Scholar
  49. 49.
    Wang R, Shan S, Chen X, Gao W (2008) Manifold-manifold distance with application to face recognition based on image set. In: IEEE conference computer vision and pattern recognition (CVPR), pp 1–8Google Scholar
  50. 50.
    Wei X, Li C-T (2013) Fixation and saccade based face recognition from single image per person with various occlusions and expressions. In: IEEE conference computer vision and pattern recognition workshops (CVPRW), pp 70–75Google Scholar
  51. 51.
    Wei X, Li C-T, Hu Y (2012) Robust face recognition under varying illumination and occlusion considering structured sparsity. In: International conference digital image computing techniques and applications (DICTA), pp 1–7Google Scholar
  52. 52.
    Wei X, Li C-T, Lei Z, Yi D, Li SZ (2014) Dynamic image-to-class warping for occluded face recognition. IEEE Trans Inf Forensics Secur 9(12):2035–2050Google Scholar
  53. 53.
    Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227Google Scholar
  54. 54.
    Zhang H, Yang J, Zhang Y, Nasrabadi NM, Huang TS (2011) Close the loop: joint blind image restoration and recognition with sparse representation prior. In: IEEE international conference computer vision (ICCV), pp 770–777Google Scholar
  55. 55.
    Zhang L, Yang M, Feng X (2011) Sparse representation or collaborative representation: which helps face recognition? In: IEEE international conference computer vision (ICCV), pp 471–478Google Scholar
  56. 56.
    Zhou S, Krueger V, Chellappa R (2003) Probabilistic recognition of human faces from video. Comput Vis Image Underst 91(1–2):214–245. Special issue on face recognitionGoogle Scholar
  57. 57.
    Zhou SK, Chellappa R, Moghaddam B (2004) Visual tracking and recognition using appearance-adaptive models in particle filters. IEEE Trans Image Process 13(11):1491–1506CrossRefGoogle Scholar
  58. 58.
    Zhu J, Cao D, Liu S, Lei Z, Li SZ (2012) Discriminant analysis with Gabor phase for robust face recognition. In: IAPR international conference biometrics (ICB), pp 13–18Google Scholar
  59. 59.
    Zou WW, Yuen PC, Chellappa R (2013) Low-resolution face tracker robust to illumination variations. IEEE Trans Image Process 22(5):1726–1739MathSciNetCrossRefGoogle Scholar
  60. 60.
    Zou X, Kittler J, Messer K (2007) Illumination invariant face recognition: a survey. In: IEEE international conference on biometrics: theory, applications, and systems (BTAS), pp 1–8Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Computing ScienceNewcastle UniversityNewcastle upon TyneUK
  2. 2.Department of Computer ScienceUniversity of WarwickCoventryUK

Personalised recommendations