Skip to main content
SpringerLink
Log in

Validation of forensic facial comparison by morphological analysis in photographic and CCTV samples

  • Original Article
  • Published:
International Journal of Legal Medicine Aims and scope Submit manuscript

Abstract

Between the ever-increasing availability of surveillance evidence and expert-based forensic facial comparison being considered admissible in court, confirming its validity is paramount. Facial comparison is most commonly conducted using morphological analysis (MA), a largely untested feature-based approach. This study aimed at validating the current recommended practice of MA in both standardised and suboptimal surveillance samples. Face pools of 175 South African males were compiled with a series of facial photographs, using images from the Wits Face Database. The first 75 face pools consisted of wildtype (unstandardised) high-quality target photographs, while the remaining 100 face pools consisted of suboptimal closed-circuit television (CCTV) target images. Target images were compared to high-quality standardised photographs. Face pools were analysed using the Facial Identification Scientific Working Group’s guidelines and feature list. Confusion matrices were used to determine the performance of MA in each cohort. MA was found highly accurate (chance-corrected accuracy (CCA): 99.1%) and reliable (κ = 0.921) in the photographic sample and less accurate (CCA: 82.6%) and reliable (κ = 0.743), in the CCTV sample. Higher false-positive and false-negative rates were noted for the CCTV sample, with the majority of errors resulting in false-negative outcomes. The decreased performance in the CCTV sample was attributed to various factors including image quality, angle of recording and lighting. Other studies testing facial comparison identified lower accuracies and reliability across various conditions. Better performance was found here and in other studies that included some form of facial feature list, reinforcing the importance of using a systematic facial feature list.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Availability of data and material

The datasets analysed during the current study are not publicly available due the sensitive nature of the Wits Face Database. The database has the identifier https://doi.org/10.17605/OSF.IO/Q8V2R. A sample of the database (consisting of one of the author’s’ facial images and recordings) is made available at https://hdl.handle.net/10539/29924. Restrictions apply to accessing the database or part thereof, which was used under license for the current study and, hence, it is not publicly available. Access is limited to formal request and conditional approval by the School of Anatomical Sciences Collections Committee, strictly for non-commercial research.

References

  1. Davis JP, Valentine T, Wilkinson C (2012) Facial image comparison. In: Wilkinson C, Rynn C (eds) Craniofacial identification, 1st edn. Cambridge University Press, New York, pp 136–153

    Chapter  Google Scholar 

  2. Lee W-L, Wilkinson C, Memon A, Houston K (2009) Matching unfamiliar faces from poor quality closed-circuit television (CCTV) footage: an evaluation of the effect of training on facial identification ability. AXIS Online J Cent Anat Hum Identif 1:19–28

    Google Scholar 

  3. Jain AK, Klare B, Park U (2012) Face matching and retrieval in forensics applications. IEEE Multimed 19:20–27. https://doi.org/10.1109/MMUL.2012.4

  4. Phillips PJ (2011) Improving face recognition technology. Computer (Long Beach Calif) 44:84–86. https://doi.org/10.1109/MC.2011.87

    Article  Google Scholar 

  5. Urbanová P (2016) Performance of distance-based matching algorithms in 3D facial identification. Egypt J Forensic Sci 6:135–151. https://doi.org/10.1016/j.ejfs.2016.04.004

    Article  Google Scholar 

  6. Davis JP, Valentine T, Davis RE (2010) Computer assisted photo-anthropometric analyses of full-face and profile facial images. Forensic Sci Int 200:165–176. https://doi.org/10.1016/j.forsciint.2010.04.012

    Article  PubMed  Google Scholar 

  7. Heyer R, Semmler C (2013) Forensic confirmation bias: the case of facial image comparison. J Appl Res Mem Cogn 2:68–70. https://doi.org/10.1016/j.jarmac.2013.01.008

    Article  Google Scholar 

  8. Caple J, Stephan CN (2016) A standardized nomenclature for craniofacial and facial anthropometry. Int J Legal Med 130:863–879. https://doi.org/10.1007/s00414-015-1292-1

    Article  PubMed  Google Scholar 

  9. Kleinberg KF, Vanezis P, Burton AM (2007) Failure of anthropometry as a facial identification technique using high-quality photographs. J Forensic Sci 52:779–783. https://doi.org/10.1111/j.1556-4029.2007.00458.x

    Article  PubMed  Google Scholar 

  10. Moreton R, Morley J (2011) Investigation into the use of photoanthropometry in facial image comparison. Forensic Sci Int 212:231–237. https://doi.org/10.1016/j.forsciint.2011.06.023

    Article  PubMed  Google Scholar 

  11. Stephan CN, Caple JM, Guyomarc’h P, Claes P (2019) An overview of the latest developments in facial imaging. Forensic Sci Res 4:10–28. https://doi.org/10.1080/20961790.2018.1519892

    Article  PubMed  Google Scholar 

  12. Facial Identification Scientific Working Group (2019) Facial comparison overview and methodology guidelines. Available at: https://fiswg.org/fiswg_facial_comparison_overview_and_methodology_guidelines_V1.0_20191025.pdf

  13. Houlton TMR, Steyn M (2018) Finding Makhubu: a morphological forensic facial comparison. Forensic Sci Int 285:13–20. https://doi.org/10.1016/J.FORSCIINT.2018.01.022

    Article  CAS  PubMed  Google Scholar 

  14. Steyn M, Pretorius M, Briers N et al (2018) Forensic facial comparison in South Africa: state of the science. Forensic Sci Int 287:190–194. https://doi.org/10.1016/J.FORSCIINT.2018.04.006

    Article  CAS  PubMed  Google Scholar 

  15. Burton AM, Wilson S, Cowan M, Bruce V (1999) Research article evidence from security surveillance. Psychol Sci 10:243–248. https://doi.org/10.1111/1467-9280.00144

    Article  Google Scholar 

  16. Bruce V, Henderson Z, Newman C, Burton AM (2001) Matching identities of familiar and unfamiliar faces caught on CCTV images. J Exp Psychol Appl 7:207–218. https://doi.org/10.1037//1076-898X.7.3.207

    Article  CAS  PubMed  Google Scholar 

  17. Burton AM, Wilson S, Cowan M, Bruce V (1999) Face recognition in poor-quality video: evidence from security surveillance. Psychol Sci 10:243–248. https://doi.org/10.1111/1467-9280.00144

    Article  Google Scholar 

  18. Henderson Z, Bruce V, Burton AM (2001) Matching the faces of robbers captured on video. Appl Cogn Psychol 15:445–464. https://doi.org/10.1002/acp.718

    Article  Google Scholar 

  19. Megreya AM, Burton AM (2008) Matching faces to photographs: poor performance in eyewitness memory (without the memory). J Exp Psychol Appl 14:364–372. https://doi.org/10.1037/a0013464

    Article  PubMed  Google Scholar 

  20. Megreya AM, Memon A, Havard C (2012) The headscarf effect: direct evidence from the eyewitness identification paradigm. Appl Cogn Psychol 26:308–315. https://doi.org/10.1002/acp.1826

    Article  Google Scholar 

  21. Wilkinson C, Evans R (2009) Are facial image analysis experts any better than the general public at identifying individuals from CCTV images? Sci Justice 49:191–196. https://doi.org/10.1016/j.scijus.2008.10.011

    Article  PubMed  Google Scholar 

  22. Burton M, White D, McNeill A (2010) The Glasgow face matching test. Behav Res Methods 42:286–291. https://doi.org/10.3758/BRM.42.1.286

    Article  PubMed  Google Scholar 

  23. Megreya AM, Bindemann M, Havard C (2011) Sex differences in unfamiliar face identification: evidence from matching tasks. Acta Psychol (Amst) 137:83–89. https://doi.org/10.1016/j.actpsy.2011.03.003

    Article  Google Scholar 

  24. Ritz-Timme S, Gabriel P, Obertovà Z et al (2011) A new atlas for the evaluation of facial features: advantages, limits, and applicability. Int J Legal Med 125:301–306. https://doi.org/10.1007/s00414-010-0446-4

    Article  PubMed  Google Scholar 

  25. Ritz-Timme S, Gabriel P, Tutkuviene J et al (2011) Metric and morphological assessment of facial features: a study on three European populations. Forensic Sci Int 207:239.e1-239.e8. https://doi.org/10.1016/j.forsciint.2011.01.035

  26. Towler A, White D, Kemp RI (2017) Evaluating the feature comparison strategy for forensic face identification. J Exp Psychol Appl 23:47–58. https://doi.org/10.1037/xap0000108

    Article  PubMed  Google Scholar 

  27. Megreya AM, Bindemann M (2018) Feature instructions improve face-matching accuracy. PLoS One 13:1–16. https://doi.org/10.1371/journal.pone.0193455

    Article  CAS  Google Scholar 

  28. Porter G, Doran G (2000) An anatomical and photographic technique for forensic facial identification. Forensic Sci Int 114:97–105. https://doi.org/10.1016/S0379-0738(00)00290-5

    Article  CAS  PubMed  Google Scholar 

  29. Facial Identification Scientific Working Group (2018) Facial image comparison feature list for morphological analysis. Available at: https://fiswg.org/FISWG_Morph_Analysis_Feature_List_v2.0_20180911.pdf

  30. Bacci N, Davimes J, Steyn M, Briers N (2020) Wits face database. Wits Institutional Repos. Environ. Dsp, In https://hdl.handle.net/10539/29924

    Google Scholar 

  31. Steyn M, Smith JR (2007) Interpretation of ante-mortem stature estimates in South Africans. Forensic Sci Int 171:97–102. https://doi.org/10.1016/j.forsciint.2006.10.006

    Article  CAS  PubMed  Google Scholar 

  32. Speckeis C (2011) Can ACE-V be validated? J Forensic Identif 61:201–209 Available at: https://www.ncjrs.gov/pdffiles1/nij/225329.pdf

    Google Scholar 

  33. R Core Team (2020) R: a language and environment for statistical computing. Available at: https://www.r-project.org/

  34. Watson PF, Petrie A (2010) Method agreement analysis: a review of correct methodology. Theriogenol 73:1167–1179. https://doi.org/10.1016/j.theriogenology.2010.01.003

    Article  CAS  Google Scholar 

  35. Kuhn M (2008) Building predictive models in R using the caret package. J Stat Softw 28:1–26. https://doi.org/10.18637/jss.v028.i05

    Article  Google Scholar 

  36. Kuhn M (2020) Caret: classification and regression training R package. Available at: https://cran.r-project.org/package=caret

  37. Dietterich TG (1998) Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput 10:1895–1923. https://doi.org/10.1007/978-3-319-50926-6_6

    Article  CAS  PubMed  Google Scholar 

  38. Norris C, McCahill M, Wood D (2004) Editorial. The growth of CCTV: a global perspective on the international diffusion of video surveillance in publicly accessible space. Surveill Soc 2:110–135. https://doi.org/10.24908/ss.v2i2/3.3369

    Article  Google Scholar 

  39. Edmond G (2013) Just truth? Carefully applying history, philosophy and sociology of science to the forensic use of CCTV images. Stud Hist Philos Sci Part C Stud Hist Philos Biol Biomed Sci 44:80–91. https://doi.org/10.1016/j.shpsc.2012.09.004

    Article  Google Scholar 

  40. Bromby M (2003) At face value? New Law J Expert Witn Suppl 28:302–303

    Google Scholar 

  41. Dewhurst SA, Hay DC, Wickham LHV (2005) Distinctiveness, typicality, and recollective experience in face recognition: a principal components analysis. Psychon Bull Rev 12:1032–1037. https://doi.org/10.3758/BF03206439

    Article  PubMed  Google Scholar 

  42. Lee K, Byatt G, Rhodes G (2000) Caricature effects, distinctiveness, and identification: testing the face-space framework. Psychol Sci 11:379–385. https://doi.org/10.1111/1467-9280.00274

    Article  CAS  PubMed  Google Scholar 

  43. Thompson WC, Taroni F, Aitken CGG (2003) How the probability of a false positive affects the value of DNA evidence. J Forensic Sci 48:2001171. https://doi.org/10.1520/jfs2001171

    Article  Google Scholar 

  44. Gibelli D, Obertová Z, Ritz-Timme S et al (2016) The identification of living persons on images: a literature review. Leg Med 19:52–60. https://doi.org/10.1016/j.legalmed.2016.02.001

    Article  CAS  Google Scholar 

  45. Damjanovski V (2014) CCTV from light to pixels, 3rd edn. Elsevier, Oxford

    Google Scholar 

  46. Ward D (2013) Testing camera height vs image quality. Pennsylvania, USA Available at: https://ipvm.com/reports/testing-camera-height

    Google Scholar 

  47. Cohen N, Gattuso J, MacLennan-Brown K (2009) CCTV Operational requirements manual. Home Office Scientific Development Branch. Sandridge, UK Available at: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/378443/28_09_CCTV_OR_Manual2835.pdf

    Google Scholar 

  48. Zhang X, Gao Y (2009) Face recognition across pose: a review. Pattern Recognit 42:2876–2896. https://doi.org/10.1016/j.patcog.2009.04.017

    Article  Google Scholar 

  49. Burton A, Bruce V, Hancock PJB (1999) From pixels to people: a model of familiar face recognition. Cogn Sci 23:1–31. https://doi.org/10.1016/S0364-0213(99)80050-0

    Article  Google Scholar 

  50. Bruce V (1994) Stability from variation: the case of face recognition The M.D. Vernon Memorial Lecture. Q J Exp Psychol Sect A 47:5–28. https://doi.org/10.1080/14640749408401141

    Article  CAS  Google Scholar 

  51. Davis JP, Maigut A, Forrest C (2019) The wisdom of the crowd: a case of post- to ante-mortem face matching by police super-recognisers. Forensic Sci Int 302:109910. https://doi.org/10.1016/j.forsciint.2019.109910

    Article  PubMed  Google Scholar 

  52. Roelofse MM, Steyn M, Becker PJ (2008) Photo identification: facial metrical and morphological features in South African males. Forensic Sci Int 177:168–175. https://doi.org/10.1016/j.forsciint.2007.12.003

    Article  CAS  PubMed  Google Scholar 

  53. Vanezis P, Lu D, Cockburn J et al (1996) Morphological classification of facial features in adult Caucasian males based on an assessment of photographs of 50 subjects. J Forensic Sci 41:13998J. https://doi.org/10.1520/jfs13998j

    Article  Google Scholar 

  54. Dodd V (2018) UK police use of facial recognition technology a failure, says report. Guard. Available at: https://www.theguardian.com/uk-news/2018/may/15/uk-police-use-of-facial-recognition-technology-failure

  55. Press Association (2018) Welsh police wrongly identify thousands as potential criminals. Guard. Available at: https://www.theguardian.com/uk-news/2018/may/05/welsh-police-wrongly-identify-thousands-as-potential-criminals

  56. Wall M (2019) Biased and wrong? Facial recognition tech in the dock. BBC News 1–12. Available at: https://www.bbc.com/news/business-48842750

  57. Grother P, Ngan M, Hanaoka K (2019) Face recognition vendor test part 3: demographic effects. Gaithersburg, MD. https://doi.org/10.6028/NIST.IR.8280

  58. Morrison R (2019) ‘Racist’ facial recognition technology used in law enforcement, banking and schools misidentifies African American and Asian people 100 times more often than whites, study shows. DailyMail Online. Available at: https://www.dailymail.co.uk/sciencetech/article-7811779/Massive-errors-facial-recognition-tech-US-study.html

  59. Belhumeur PN, Jacobs DW, Kriegman DJ, Kumar N (2013) Localizing parts of faces using a consensus of exemplars. IEEE Trans Pattern Anal Mach Intell 35:2930–2940. https://doi.org/10.1109/TPAMI.2013.23

  60. Burgos-Artizzu XP, Perona P, Dollar P (2013) Robust face landmark estimation under occlusion. Proc IEEE Int Conf Comput Vis:1513–1520. https://doi.org/10.1109/ICCV.2013.191

  61. Sagonas C, Antonakos E, Tzimiropoulos G et al (2015) 300 faces In-The-Wild Challenge: database and results. Image Vis Comput 47:3–18. https://doi.org/10.1016/j.imavis.2016.01.002

  62. Ellis HD, Shepherd JW, Davies GM (1979) Identification of familiar and unfamiliar faces from internal and external features: some implications for theories of face recognition. Perception 8:431–439. https://doi.org/10.1068/p080431

    Article  CAS  PubMed  Google Scholar 

  63. Megreya AM, Burton AM (2006) Unfamiliar faces are not faces evidence from a matching task. Mem Cognit 34:865–876. https://doi.org/10.3758/BF03193433

    Article  PubMed  Google Scholar 

  64. Bindemann M, Johnston RA (2017) Understanding how unfamiliar faces become familiar: Introduction to a special issue on face learning. Q J Exp Psychol 70:859–862. https://doi.org/10.1080/17470218.2016.1267235

    Article  Google Scholar 

  65. Facial Identification Scientific Working Group (2012) FISWG guidelines for facial comparison methods. Available at: https://www.fiswg.org/FISWG_GuidelinesforFacialComparisonMethods_v1.0_2012_02_02.pdf

  66. Alenezi HM, Bindemann M, Fysh MC (2015) Johnston RA (2015) Face matching in a long task: Enforced rest and desk-switching cannot maintain identification accuracy. PeerJ 3:e1184. https://doi.org/10.7717/peerj.1184

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Thanks are due to all the participants who agreed to be photographed and recorded for the development of the Wits Face Database from which the face pool samples were composed. Special thanks also go to Joshua Davimes for his instrumental contributions in developing the Wits Face Database. We are also especially grateful towards Tamara Lottering for her assistance in composing the face pools and Gideon LeRoux for his assistance in the capturing and extracting of the CCTV recordings from the University security systems. Thanks are also due to the volunteers who aided in participant recruitment: Jesse Fredericks, Kiveshen Pillay, Rethabile Masiu, Sameerah Sallie, Daniel Munesamy, Laurette Joubert, Jordan Swiegers, Betty Mkabela, Johannes P. Meyer, Amy Spies, Natasha Loubser, Nicole Virgili, Dan-Joel Lukumbi, Tamara Lottering, Mathabatha Ntjie, Claudia Landsman, Raheema Dalika, Merete Goosen, Stephanie Souris, Rabelani Negota, Mahlatse Mahasha, and Jessica Manavhela.

Funding

The current study was conducted with support from the South African National Research Foundation (NRF) and the J.J.J. Smieszeck Fellowship from the School of Anatomical Sciences, University of the Witwatersrand (DAAD-NRF and J.J.J. Smieszeck Fellowship funds awarded to N. Bacci (Grant No.: 11858) and NRF funds awarded to N. Briers as part of the Improving Methodologies and Practices in Craniofacial Identification (Grant No.: CSUR160425163022; UID: 106031). Any opinions, findings, and conclusions or recommendations expressed in this study are those of the authors and therefore the NRF and University of the Witwatersrand, Johannesburg, do not accept any liability in regard thereto.

Author information

Authors and Affiliations

  1. Human Variation and Identification Research Unit, School of Anatomical Sciences, University of the Witwatersrand, 7 York Road, Parktown, Johannesburg, 2193, South Africa

    Nicholas Bacci, Tobias M. R. Houlton, Nanette Briers & Maryna Steyn

Authors
  1. Nicholas Bacci
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tobias M. R. Houlton
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nanette Briers
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Maryna Steyn
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualisation: Nicholas Bacci, Maryna Steyn, and Nanette Briers. Methodology: Nicholas Bacci, Maryna Steyn, Nanette Briers, and Tobias Houlton. Formal analysis and investigation: Nicholas Bacci and Tobias Houlton. Writing (original draft preparation): Nicholas Bacci. Writing (review and editing): Nicholas Bacci, Maryna Steyn, Nanette Briers, and Tobias Houlton. Funding acquisition: Nicholas Bacci, Maryna Steyn, and Nanette Briers. Resources: Maryna Steyn and Nanette Briers. Supervision: Maryna Steyn and Nanette Briers.

Corresponding author

Correspondence to Nicholas Bacci.

Ethics declarations

Ethics approval and consent to participate

The study was unconditionally approved by the Human Research Ethics Committee (Medical) of the University of the Witwatersrand, Johannesburg. Ethics clearance certificate number: M171026. All participants included in the study signed an informed consent form granting the authors the use of their data.

Consent for publication

No participant information or photographs are published as part of the study, as agreed upon in the aforementioned informed consent signed for participation.

Conflict of interest

The authors declare no conflict of interest.

Code availability

Not applicable

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

ESM 1

(PDF 100 kb)

ESM 2

(PDF 231 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bacci, N., Houlton, T.M.R., Briers, N. et al. Validation of forensic facial comparison by morphological analysis in photographic and CCTV samples. Int J Legal Med 135, 1965–1981 (2021). https://doi.org/10.1007/s00414-021-02512-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00414-021-02512-3

Keywords

Search

Navigation