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The identification of living persons on images: A literature review
Abstract
Personal identification in the forensic context commonly concerns unknown decedents. However, recently there has been an increase in cases which require identification of living persons, especially from surveillance systems. These cases bring about a relatively new challenge for forensic anthropologists and pathologists concerning the selection of the most suitable methodological approaches with regard to the limitations of the photographic representation of a given person for individualization and identity. Facial features are instinctively the primary focus for identification approaches. However, other body parts (e.g. hands), and body height and gait (on videos) have been considered in cases of personal identification. This review aims at summarizing the state-of-the-art concerning the identification of the living on images and videos, including a critical evaluation of the advantages and limitations of different methods. Recommendations are given in order to aid forensic practitioners who face cases of identification of living persons.
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The majority of available image data in law enforcement applications is of low to poor quality.
“The importance of MA-based FFC is especially noteworthy in law enforcement applications, where the majority of available image data is of low to poor quality [113].”
From Forensic Facial Comparison: Current Status, Limitations, and Future Directions (2021) · cites this paper as [113]
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(2008) · doi:10.1007/978-0-387-71041-9 · Cited by 1,406
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Research works printed but not linked: 95
- Kim, 2012, A survey and proposed framework on the soft biometrics technique for human identification in intelligent video surveillance system, J. Biomed. Biotechnol., 2012, 614146, 10.1155/2012/614146 · DOI
- Lyon, 2008, Biometrics, identification and surveillance, Bioethics, 22, 499, 10.1111/j.1467-8519.2008.00697.x · DOI
- Vanezis, 1996, Morphological classification of facial features in adult Caucasian males based on an assessment of photographs of 50 subjects, J. Forensic Sci., 41, 786, 10.1520/JFS13998J · DOI
- Ritz-Timme, 2010, A new atlas for the evaluation of facial features: advantages, limits and applicability, Int. J. Legal Med., 125, 301, 10.1007/s00414-010-0446-4 · DOI
- Porter, 2000, An anatomical and photographic technique for forensic facial identification, Forensic Sci. Int., 114, 97, 10.1016/S0379-0738(00)00290-5 · DOI
- Kleinberg, 2007, Failure of anthropometry as a facial identification technique using high-quality photographs, J. Forensic Sci., 44, 779, 10.1111/j.1556-4029.2007.00458.x · DOI · Paper page
- Halberstein, 2001, The application of anthropometric indices in forensic photography: three case studies, J. Forensic Sci., 46, 1438, 10.1520/JFS15168J · DOI
- Moreton, 2011, Investigation into the use of photoanthropometry in facial image comparison, Forensic Sci. Int., 212, 231, 10.1016/j.forsciint.2011.06.023 · DOI
- Lynnerup, 2003, Facial image identification using Photomodeler, Legal Med., 5, 156, 10.1016/S1344-6223(03)00054-3 · DOI
- Tome, 2013, Identification using face regions: application and assessment in forensic scenarios, Forensic Sci. Int., 233, 75, 10.1016/j.forsciint.2013.08.020 · DOI · Paper page
- Davis, 2010, Computer assisted photo-anthropometric analyses of full-face and profile facial images, Forensic Sci. Int., 200, 165, 10.1016/j.forsciint.2010.04.012 · DOI
- Kleinberg, 2012, A study of quantitative comparisons of photographs and video images based on landmark derived feature vectors, Forensic Sci. Int., 219, 248, 10.1016/j.forsciint.2012.01.014 · DOI
- Catterick, 1992, Facial measurements as an aid to recognition, Forensic Sci. Int., 56, 23, 10.1016/0379-0738(92)90142-J · DOI
- Mane, 2010, Anthropometric and anthroposcopic analysis of different shapes of faces in group of Indian population: a pilot study, J. Forensic Legal Med., 17, 421, 10.1016/j.jflm.2010.09.001 · DOI
- Roelofse, 2008, Photo identification: facial metrical and morphological features in South African males, Forensic Sci. Int., 177, 168, 10.1016/j.forsciint.2007.12.003 · DOI
- Ritz-Timme, 2011, Metrical and morphological assessment of facial features: a study on a European population, Forensic Sci. Int., 207, 10.1016/j.forsciint.2011.01.035 · DOI
- Yoshino, 2000, Computer-assisted facial image identification system using a 3-D physiognomic range finder, Forensic Sci. Int., 109, 225, 10.1016/S0379-0738(00)00149-3 · DOI
- Biwasaka, 2010, Application of computerised correction method for optical distortion of two-dimensional facial image in superimposition between three-dimensional and two-dimensional facial images, Forensic Sci. Int., 197, 97, 10.1016/j.forsciint.2009.12.063 · DOI
- Cummaudo, 2013, Pitfalls at the root of facial assessment on photographs: a quantitative study of accuracy in positioning facial landmarks, Int. J. Legal Med., 127, 699, 10.1007/s00414-013-0850-7 · DOI
- Ventura, 2004, Computerised anthropomorphometric analysis of images: case report, Forensic Sci. Int., 146, S211, 10.1016/j.forsciint.2004.09.065 · DOI
- Sinha, 1998, A symmetry perceiving adaptive neural network and facial image recognition, Forensic Sci. Int., 98, 67, 10.1016/S0379-0738(98)00137-6 · DOI
- Kovacs, 2006, Three-dimensional recording of the human face with a 3D laser scanner, J. Plast. Reconstr. Aesthet. Surg., 59, 1193, 10.1016/j.bjps.2005.10.025 · DOI
- Lynnerup, 2009, Facial recognition and laser surface scan: a pilot study, Forensic Sci. Med. Pathol., 5, 167, 10.1007/s12024-009-9094-8 · DOI
- De Angelis, 2009, A new computer-assisted technique to aid personal identification, In. J. Legal Med., 123, 351, 10.1007/s00414-008-0311-x · DOI
- Yoshino, 2005, A new retrieval system for a database of 3D facial images, Forensic Sci. Int., 148, 113, 10.1016/j.forsciint.2004.04.074 · DOI
- Yoshino, 2002, Individual identification of disguised faces by morphometrical matching, Forensic Sci. Int., 127, 97, 10.1016/S0379-0738(02)00115-9 · DOI
- Li, 2006, Biometric identification using 3D face scans, Biomed. Sci. Instrum., 42, 320
- Arca, 2012, Improving automatic face recognition with user interaction, J. Forensic Sci., 57, 765, 10.1111/j.1556-4029.2011.02018.x · DOI
- Kustàr, 2013, FACE-R – a 3D database of 400 living individuals’ full head CT- and face scans and preliminary GMM analysis for craniofacial reconstruction, J.Forensic Sci., 58, 1420, 10.1111/1556-4029.12215 · DOI
- Lynnerup, 2010, Matching profiles o masked perpetrators: a pilot study, Med. Sci. Law, 50, 200, 10.1258/msl.2010.010013 · DOI
- Vanezis, 1997, Facial image comparison of crime suspects using video superimposition, Sci. Justice, 36, 27, 10.1016/S1355-0306(96)72551-0 · DOI
- Fraser, 2003, A Japanese computer-assisted facial identification system successfully identifies non-Japanese faces, Forensic Sci. Int., 135, 122, 10.1016/S0379-0738(03)00182-8 · DOI
- Goos, 2006, 2D/3D image (facial) comparison using camera matching, Forensic Sci. Int., 163, 10, 10.1016/j.forsciint.2005.11.004 · DOI
- Cattaneo, 2012, Personal identification by the comparison of facial profiles: testing the reliability of a high resolution 3D–2D comparison model, Int. J. Legal Med., 57, 182
- Atsuchi, 2013, Assessment of some problematic factors in facial image identification using a 2D/3D superimposition technique, Legal Med., 15, 244, 10.1016/j.legalmed.2013.06.002 · DOI
- Kieser, 2007, The uniqueness of the human anterior dentition: a geometric morphometric analysis, J. Forensic Sci., 52, 671, 10.1111/j.1556-4029.2007.00403.x · DOI
- De Angelis, 2007, Dental superimposition: a pilot study for standardising the method, Int. J. Legal Med., 121, 501, 10.1007/s00414-007-0198-y · DOI
- Silva, 2008, Forensic odontology identification using smile photograph analysis – case reports, J. Forensic Odontostomatol., 27, 12
- Purkait, 2008, A test of individuality of human external ear pattern: its application in the field of personal identification, Forensic Sci. Int., 178, 112, 10.1016/j.forsciint.2008.02.009 · DOI
- Cameriere, 2011, Ear identification, J. Forensic Sci., 56, 1010, 10.1111/j.1556-4029.2011.01778.x · DOI
- A.J. Hoogstrate, H. Van Den Heuvel, E. Huyben, Ear identification based on surveillance camera images, in: Proceedings of the Forensic Science Society Meeting “European Connections”, April 2001. · DOI
- Venkatesh, 2011, Cheiloscopy: an aid for personal identification, J. Forensic Dent. Sci., 3, 67, 10.4103/0975-1475.92147 · DOI
- T. Izumi, Y. Uerki, Improvement of precision by introducing vitality degree to the height measurement system using stereo vision, in: Proceeding of SICE annual conference, Sapporo, August 4–6 2004.
- A. Criminisi, A. Zissermann, L. Van Gool, A new approach to obtain height measurement from video, in: Proceedings of SPIE, vol. 3576, Boston, Massachussets, USA, 1–6 November 1998.
- Alberink, 2008, Obtaining confidence intervals and likelihood ratios for body height estimations in images, Forensic Sci. Int., 177, 228, 10.1016/j.forsciint.2008.01.005 · DOI
- C. BenAbdelkader, R. Cutler, L. Davis, Person identification using automatic height and stride estimation, in: Proceedings of 16th International Conference on Pattern Recognition, vol. 4, 2002, pp. 377–380. · DOI
- Edelman, 2009, Comparison of body height estimation using bipeds or cylinders, Forensic Sci. Int., 188, 64, 10.1016/j.forsciint.2009.03.013 · DOI
- van den Hout, 2010, A hierarchical model for body height estimation in images, Forensic Sci. Int., 197, 48, 10.1016/j.forsciint.2009.12.020 · DOI
- Lee, 2008, Efficient height measurement method of surveillance camera image, Forensic Sci. Int., 177, 17, 10.1016/j.forsciint.2007.10.008 · DOI
- De Angelis, 2007, New method for height estimation of subjects represented in photograms taken from video surveillance systems, Int. J. Legal Med., 121, 489, 10.1007/s00414-007-0176-4 · DOI
- Edelman, 2010, Comparison of the performance of two methods for height estimation, J. Forensic Sci., 55, 358, 10.1111/j.1556-4029.2009.01296.x · DOI
- Hoogeboom, 2009, Body height measurements in images, J. Forensic Sci., 54, 1365, 10.1111/j.1556-4029.2009.01179.x · DOI
- Ng, 2014, Development of vision based multiview gait recognition system with MMUGait database, Sci. World J., 2014, 376569, 10.1155/2014/376569 · DOI
- Zeng, 2012, Human gait recognition vie deterministic learning, Neural Network, 35, 92, 10.1016/j.neunet.2012.07.012 · DOI
- Muramatsu, 2015, Gait-based person recognition using arbitrary view transformation model, IEEE Trans. Image Process, 24, 140, 10.1109/TIP.2014.2371335 · DOI
- Liu, 2014, Gait phase varies over velocities, Gait Posture, 39, 756, 10.1016/j.gaitpost.2013.10.009 · DOI
- Kusakunniran, 2012, Gait recognition across various walking speeds using higher order shape configuration based on a differential composition model, IEEE Trans. Syst. Man Cybern. B Cybern., 42, 1654, 10.1109/TSMCB.2012.2197823 · DOI
- Chen, 2014, Gait correlation analysis based human identification, Sci. World J.
- Birch, 2013, The development of a tool for assessing the quality of closed circuit camera footage for use in forensic gait analysis, J. Forensic Legal Med., 20, 915, 10.1016/j.jflm.2013.07.005 · DOI
- Larsen, 2008, Gait analysis in forensic medicine, J. Forensic Sci., 53, 1149, 10.1111/j.1556-4029.2008.00807.x · DOI
- Lynnerup, 2005, Person identification by gait analysis and photogrammetry, J. Forensic Sci., 50, 112, 10.1520/JFS2004054 · DOI
- Yang, 2014, Variability and similarity of gait as evaluated by joint angles: implications for forensic gait analysis, J. Forensic Sci., 59, 494, 10.1111/1556-4029.12322 · DOI
- O’Dwyer, 2009, Independent assessment of pattern and offset variability of time series waveforms, Gait Posture, 29, 285, 10.1016/j.gaitpost.2008.09.005 · DOI
- Yang, 2014, Influence of velocity on variability in gait kinematics: implications for recognition in forensic science, J. Forensic Sci., 59, 1242, 10.1111/1556-4029.12490 · DOI
- Tafazzoli, 2010, Model-based human gait recognition using leg and arm movements, Eng. Appl. Artif. Intell., 23, 1237, 10.1016/j.engappai.2010.07.004 · DOI
- Birch, 2013, The identification of individuals by observational gait analysis using closed circuit television footage, Sci. Justice, 53, 339, 10.1016/j.scijus.2013.04.005 · DOI
- Birch, 2014, The effect of frame rate on the ability of experienced gait analysts to identify characteristics of gait from closed circuit television footage, Sci. Justice, 54, 159, 10.1016/j.scijus.2013.10.002 · DOI
- Larsen, 2008, Variability of bodily measures of normally dressed people using PhotoModeler Pro 5, J. Forensic Sci., 53, 1393, 10.1111/j.1556-4029.2008.00874.x · DOI
- Scoleri, 2014, Effects of garments on photoanthropometry of body parts: application to stature estimation, Forensic Sci. Int., 237, 10.1016/j.forsciint.2013.12.038 · DOI
- Malone, 2014, Photographic analyses using skin detail of the hand: a methodology and evaluation, J. Forensic Sci.
- Slot, 2014, The possibilities and limitations of forensic hand comparison, J. Forensic Sci., 59, 1559, 10.1111/1556-4029.12542 · DOI
- Chattoppadhyay, 2012, Identification from dorsal finger pattern: a new approach, Med. Sci. Law, 52, 17, 10.1258/msl.2011.010145 · DOI
- Thomas, 1967, The longitudinal striation of the human nails as a means of identification, J. Forensic Med., 14, 113
- Zhou, 2014, Palm-vein classification based on principal orientation features, PLoS One, 9, e112429, 10.1371/journal.pone.0112429 · DOI
- Xi, 2013, Finger vein recognition with personalized feature selection, Sensors, 13, 11243, 10.3390/s130911243 · DOI
- Yu, 2009, Finger-vein image recognition combining modified Hausdorff distance with minutiae feature matching, Interdiscip. Sci. Comput. Life Sci., 1, 280, 10.1007/s12539-009-0046-5 · DOI
- Komar, 2008, Tattoo types and frequencies in New Mexican white hispanics and white non-hispanics, Am. J. Forensic Med. Pathol., 29, 285, 10.1097/PAF.0b013e31818736f3 · DOI
- Saks, 2010, Forensic identification: from a faith-based “Science” to a scientific science, Forensic Sci. Int., 201, 14, 10.1016/j.forsciint.2010.03.014 · DOI
- Buhmann, 1999, Standards für die anthropologische identifkation lebender Personen auf Grund von Bilddokumenten, Grundlagen, Kriterien und Verfahrensregeln fur Gutachten, Anthr. Anz., 57, 185
- Smeets, 2010, Objective 3D face recognition: evolution, approaches and challenges, Forensic Sci. Int., 201, 125, 10.1016/j.forsciint.2010.03.023 · DOI · Paper page
- R. Sala, A. Cantatore, A. Cigada, E. Zappa, Personal identification and minimum requirements on image metrological features, in: Proceedings of the IMEKO, IEEE, SICE, the 2nd International Symposium on Measurement, Analysis and Modeling of Human Functions, Genova, Italy, June 14–16, 2004.
- Sforza, 2010, A three-dimensional study of facial mimicry in healthy young adults, J. Craniomaxillofacial Surg., 38, 409, 10.1016/j.jcms.2009.10.018 · DOI
- R.T. Collins, R. Gross, J. Shi, Silhouette-based human identification from body shape and gait, in: Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition, 20–21 May, 2002, pp. 351–356. · DOI
- Wang, 2003, Silhouette analysis-based gait recognition for human identification, IEEE Trans. Pattern Anal. Mach. Intell., 25, 1505, 10.1109/TPAMI.2003.1251144 · DOI
- Lynnerup, 2014, Gait as evidence, IET Biometrics, 3, 47, 10.1049/iet-bmt.2013.0090 · DOI
- Abate, 2007, 2D and 3D face recognition: a survey, Pattern Recogn. Lett., 28, 1885, 10.1016/j.patrec.2006.12.018 · DOI
- Grivas, 2008, Kumho, Daubert, and the nature of scientific inquiry: implications for forensic anthropology, J. Forensic Sci., 53, 771, 10.1111/j.1556-4029.2008.00771.x · DOI
- Mallett, 2013, Forensic facial comparison: issues of admissibility in the development of novel analytical technique, J. Forensic Sci., 58, 859, 10.1111/1556-4029.12127 · DOI
- Johnston, 2009, Familiar and unfamiliar face recognition: a review, Memory, 17, 577, 10.1080/09658210902976969 · DOI
- Hancock, 2000, Recognition of unfamiliar faces, Trends Cogn. Sci., 4, 330, 10.1016/S1364-6613(00)01519-9 · DOI · Paper page
- Van der Linde, 2010, A combinatorial study of pose effects in unfamiliar face recognition, Vision Res., 50, 522, 10.1016/j.visres.2009.12.012 · DOI
- McKone, 2012, A critical review of the development of face recognition: experience is less important than previously believed, Cogn. Neuropsychol., 29, 174, 10.1080/02643294.2012.660138 · DOI
- McBain, 2009, Females excel at basic face perception, Acta Psychol. (Amst), 130, 168, 10.1016/j.actpsy.2008.12.005 · DOI
- Wilbraham, 2008, Can low level image differences account for the ability of human observers to discriminate facial identity?, J. Visual., 8, 1
- Wilkinson, 2009, Are facial image analysis experts any better than the general public at identifying individuals from CCTV images?, Sci. Justice, 49, 191, 10.1016/j.scijus.2008.10.011 · DOI · Paper page
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Forensic Facial Comparison: Current Status, Limitations, and Future Directions
Bacci, N., Davimes, J. G., Steyn, M., & Briers, N. (2021) · Biology · doi:10.3390/biology10121269 · Cited by 26
Validation of forensic facial comparison by morphological analysis in photographic and CCTV samples
Bacci, N., Houlton, T. M. R., Briers, N., & Steyn, M. (2021) · International Journal of Legal Medicine · doi:10.1007/s00414-021-02512-3 · Cited by 23
Metadata
- Authors
- Gibelli, D., Obertová, Z., Ritz-Timme, S., Gabriel, P., Arent, T., Ratnayake, M., De Angelis, D., & Cattaneo, C.
- Venue
- Legal Medicine
- Publisher
- Elsevier BV
- Published
- 2016-03
- Volume
- 19
- Pages
- 52-60
- ISSN
- 1344-6223
- Cited by
- 38
- References
- 1
- Language
- en
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