Skip to main content
Springer Nature Link
Log in

On Using Soft Biometrics in Forensic Investigation

  • Chapter
  • First Online:
Handbook of Biometrics for Forensic Science

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

  • 2148 Accesses

Abstract

This chapter addresses the usage of biometric recognition tools in the context of forensic investigations . In particular, the authors are concerned with the extraction of evidence from video sequences captured by surveillance cameras. In such scenarios many of the biometric traits traditionally used for recognition purposes, such as fingerprints, palmprints or iris, are not available. Therefore, the focus is on the extraction of soft biometrics, which encompasses personal characteristics used by humans to recognize or help to recognize an individual. This work starts by reviewing how forensic casework relying on surveillance video information is conducted nowadays. Then, a software platform , BioFoV, is proposed to automate many of the required procedures and including some initial implementation of soft biometric extraction tools. Furthermore, novel biometric methods to analyze human gait and facial traits are described and experimentally validated as a demonstration of future perspectives in soft biometrics.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

eBook
USD 12.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    Microsoft, Redmond, Washington, USA.

References

  1. Ariyanto G, Nixon M (2012) Marionette mass-spring model for 3D gait biometrics. In: Proceedings of the International Conference on Biometrics (ICB), pp 354–359

    Google Scholar 

  2. Baluja S, Rowley H (2007) Boosting sex identification performance. Int J Comput Vision 71:111–119

    Article  Google Scholar 

  3. Baumgart BG (1974) Geometric modeling for computer vision. Stanford University

    Google Scholar 

  4. Biber K (2009) Visual jurisprudence: the dangers of photographic identification evidence. CJM 78:35–37

    Google Scholar 

  5. Bouchrika I, Goffredo M, Carter J, Nixon M (2011) On using gait in forensic biometrics. J Forensic Sci 56(4):882–889

    Article  Google Scholar 

  6. Bouguet J-Y (2011) MATLAB calibration tool. http://www.vision.caltech.edu/bouguetj/calib_doc/. Accessed 26 Mar 2015

  7. Breiman L (2001) Random forests. Mach Learn 45:5–32

    Article  MATH  Google Scholar 

  8. Dantcheva A, Velardo C , D’angelo A, Dugelay JL (2011) Bag of soft biometrics for person identification: new trends and challenges. Multimed Tools Appl 51(2):739–777

    Google Scholar 

  9. Fu Y, Guo G, Huang TS (2010) Age synthesis and estimation via faces: a survey. IEEE Trans Pattern Anal Mach Intell (T-PAMI), 32(11):1955–1976

    Google Scholar 

  10. Fu S, He H, Hou Z (2014) Learning race from face: a survey. IEEE Trans Pattern Anal Mach Intell (TPAMI)

    Google Scholar 

  11. Furukawa Y, Ponce J (2010) Accurate, dense and robust multi-view stereopsis, vol 32

    Google Scholar 

  12. Gallagher AC, Chen T (2009) Understanding images of groups of people. In: Proceedings of IEEE CVPR

    Google Scholar 

  13. Guan Y, Li C-T (2013) A robust speed-invariant gait recognition system for walker and runner identification. In: The 6th IAPR international conference on biometrics: IAPR, pp 1–8

    Google Scholar 

  14. Guo G, Mu G, Fu Y, Huang T (2009) Human age estimation using bio-inspired features. In: CVPR’09, pp 112–119

    Google Scholar 

  15. Hadid A, Pietikäinen M, Li SZ (2007) Learning personal specific facial dynamics for face recognition from videos. In: IEEE international workshop on analysis and modeling of faces and gestures (in conjunction with ICCV 2007), pp 1–15

    Google Scholar 

  16. Hadid A, Pietikäinen M (2008) Combining motion and appearance for gender classification from video sequences. In: 19th international conference on pattern recognition (ICPR 2008), p 4

    Google Scholar 

  17. Han H, Jain AK (2014) Age, Gender and Race Estimation from Unconstrained Face Images, MSU Technical report (2014): MSU-CSE-14-5

    Google Scholar 

  18. Harris C, Stephens M (1988) A combined corner and edge detector. In: Alvey vision conference: Manchester, UK, p 50

    Google Scholar 

  19. Hartley R, Zisserman A (2003) Multiple view geometry in computer vision, 2nd edn. Cambridge University Press

    Google Scholar 

  20. Hautamaki S (2011) Forevid: an open source software for forensic video analysis. MSc Thesis, Tampere University of Technology

    Google Scholar 

  21. Horn BKP (1970) Shape from shading: a method for obtaining the shape of a smooth opaque object from one view

    Google Scholar 

  22. Iwama H, Muramatsu D, Makihara Y, Yagi Y (2012) Gait-based person-verification system for forensics. In: 2012 IEEE 5th international conference on biometrics: theory, applications and systems (BTAS), Sept 2012, pp 113–120

    Google Scholar 

  23. Jain A, Dass S, Nandakumar K (2004) Soft biometric traits for personal recognition systems. In: Proceedings of the international conference on biometric authentication, ICBA, LNCS 3072, pp 731–738

    Google Scholar 

  24. Jain A, Russ A (2015) Bridging the gap: from biometrics to forensics. Philos Trans Roy Soc B

    Google Scholar 

  25. Kanade T, Cohn JF, Tian Y (2000) Comprehensive database for facial expression analysis. In: IEEE international conference on automatic face and gesture recognition, pp 46–53

    Google Scholar 

  26. Klare B, Klum S, Klontz J, Taborsky E, Akgul T, Jain AK (2014) Suspect identification based on descriptive facial attributes. In: Proceedings of the international joint conference on biometrics

    Google Scholar 

  27. Klontz JaC, Jain AK (2013) A case study of automated face recognition: the Boston marathon bombings suspects. IEEE Comput 46(11):91–94

    Google Scholar 

  28. Kwon YH, da Vitoria Lobo N (1994) Age classification from facial images. In: CVPR’94, pp 762–767

    Google Scholar 

  29. Lanitis A, Taylor C, Cootes T (2002) Toward automatic simulation of aging effects on face images. TPAMI 24(4):442–455

    Article  Google Scholar 

  30. Larsen PK, Hansen L, Simonsen EB, Lynnerup N (2008) Variability of bodily measures of normally dressed people using PhotoModeler® Pro 5. J Forensic Sci 53:1393–1399

    Article  Google Scholar 

  31. Larsen PK, Lynnerup N, Henriksen M, Alkjær T, Simonsen EB (2010) Gait recognition using joint moments, joint angles, and segment angles. J Forensic Biomech 1:7

    Article  Google Scholar 

  32. Larsen PK, Simonsen EB, Lynnerup N (2008) Gait analysis in forensic medicine. J Forensic Sci 53:1149–1153

    Article  Google Scholar 

  33. Lienhart R, Maydt J (2002) An extended set of haarlike features for rapid object detection. In: Proceedings of the international conference on image processing, Rochester, USA

    Google Scholar 

  34. Lucy D (2005) Introduction to statistics for forensic scientists. Wiley

    Google Scholar 

  35. Makinen E, Raisamo R (2008) An experimental comparison of gender classification methods. Pattern Recogn Lett 29(10):1544–1556

    Article  Google Scholar 

  36. Marr D, Hildreth E (1980) Theory of Edge Detection. Proc Roy Soc B: Biol Sci 207:31

    Article  Google Scholar 

  37. Moghaddam B, Yang M-H (2002) Learning gender with support faces. IEEE Trans Pattern Anal Mach Intell 24(5):707–711

    Article  Google Scholar 

  38. Ng CB, Tay YH, Goi B-M (2012) Recognizing human gender in computer vision: a survey. PRICAI 335–346

    Google Scholar 

  39. Ocean Systems (2015) Ocean systems forensic video and image analysis solutions. http://www.oceansystems.com. Accessed 26 Mar 2015

  40. Ojala T, Pietikäinen M, Mäenpää T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24:971–987

    Article  MATH  Google Scholar 

  41. OpenCV (2015) Open source computer vision and machine learning software library. http://www.opencv.org. Accessed 26 Mar 2015

  42. Panis G, Lanitis A (2014) An overview of research activities in facial age estimation using the FG-NET aging database. In: International ECCV workshop on soft biometrics

    Google Scholar 

  43. Qt (2015) Cross-platform application and UI framework. http://www.qt.io/, Accessed 26 Mar 2015

  44. Reid DA, Nixon MS, Stevenage SV (2013) Soft biometrics; human identification using comparative descriptions. IEEE Trans Biom Compend Pattern Anal Mach intell. 36(6)

    Google Scholar 

  45. Ricanek K, Tesafaye T (2006) MORPH: a longitudinal image database of normal adult age-progression. In: Proceedings of FG

    Google Scholar 

  46. Sanderson C, Paliwal KK (2003) Noise compensation in a person verification system using face and multiple speech feature. Pattern Recogn 36(2):293–302

    Article  Google Scholar 

  47. Thornton J, Peterson J (2002) The general assumptions and rationale of forensic identification. In: Modern scientific evidence: the law and science of expert testimony, vol 3. West Publishing Company

    Google Scholar 

  48. Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition, 2001. CVPR 2001, vol 1, pp I–511–I–518

    Google Scholar 

  49. Will PM, Pennington KS (1971) Grid coding: a preprocessing technique for robot and machine vision. Artif Intell 2:319–329

    Article  Google Scholar 

  50. Xu Z, Schwarte R, Heinol H-G, Buxbaum B, Ringbeck T (1998) Smart pixel: photonic mixer device (PMD); new system concept of a 3D-imaging camera-on-a-chip. In: Eheung EHM (ed) Proceedings: M2VIP ‘98; Nanjing, China, 10–12 Sept 1998, Hong Kong. pp 259–64

    Google Scholar 

  51. Zhang Z (2000) A flexible new technique for camera calibration. IEEE Trans Pattern Anal Mach Intell 22(11):1330–1334

    Article  Google Scholar 

  52. Zhao G, Pietikäinen M (2007) Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans Pattern Anal Mach Intell 29(6):915–928

    Article  Google Scholar 

  53. Zivkovic Z (2004) Improved adaptive Gaussian mixture model for background subtraction. In: Proceedings of the 17th international conference on pattern recognition, 2004. ICPR 2004, Aug 2004, vol 2, pp 28–31

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Instituto de Telecomunicações, Instituto Superior Técnico, Universidade de Lisboa, Torre Norte, 10-15, 1049-001, Lisbon, Portugal

    Paulo Lobato Correia & Miguel Almeida

  2. University of Copenhagen, Copenhagen, Denmark

    Peter K. Larsen

  3. University of Oulu, Oulu, Finland

    Abdenour Hadid

  4. Danish Institute of Fire and Security Technology, Hvidovre, Denmark

    Martin Sandau

Authors
  1. Paulo Lobato Correia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peter K. Larsen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abdenour Hadid
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Martin Sandau
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Miguel Almeida
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Paulo Lobato Correia .

Editor information

Editors and Affiliations

  1. University of Sassari, Alghero, Sassari, Italy

    Massimo Tistarelli

  2. University of Lausanne, Lausanne, Switzerland

    Christophe Champod

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Correia, P.L., Larsen, P.K., Hadid, A., Sandau, M., Almeida, M. (2017). On Using Soft Biometrics in Forensic Investigation. In: Tistarelli, M., Champod, C. (eds) Handbook of Biometrics for Forensic Science. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-50673-9_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-50673-9_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50671-5

  • Online ISBN: 978-3-319-50673-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics