The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
Validation of forensic facial comparison by morphological analysis in photographic and CCTV samples
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.
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Across all of the studies, chance-corrected accuracy (CCA) was the best and most representative measure of accuracy, compared to hit rate and balanced accuracy.
“Across all of our studies, the best measure of accuracy was found to be the chance corrected accuracy (CCA) [21,48,49].”
From Forensic Facial Comparison: Current Status, Limitations, and Future Directions (2021) · cites this paper as [21]
The performance of MA in faces disguised with sunglasses was markedly high (90.4%), surpassing performance under the same standard CCTV conditions without sunglasses (82.6%), but not better than photographic (99.1%) or eye-level CCTV data (97.3%).
“Overall, the performance of MA in faces disguised with sunglasses was markedly high (90.4%) [49], in fact surpassing the performance of facial comparison under the same standard CCTV conditions without sunglasses (82.6%), but not better than the photographic (99.1%) [21] or eye-level CCTV data (97.3%) [48] (Figure 2).”
From Forensic Facial Comparison: Current Status, Limitations, and Future Directions (2021) · cites this paper as [21]
In an analysis of 100 face sets (n = 1000 comparisons) with standard digital CCTV recordings as the target image, MA had lower accuracy (82.6%) and reliability (74.3%).
“In the analysis of 100 face sets (n = 1000 comparisons) with standard digital CCTV recordings as the target image, a lower accuracy (82.6%) and reliability (74.3%) were noted [21] (Figure 2).”
From Forensic Facial Comparison: Current Status, Limitations, and Future Directions (2021) · cites this paper as [21]
Morphological analysis using the FISWG feature list on the Wits Face Database was highly accurate and reliable when comparing optimal standardized photographs to wildtype unstandardized photographs.
“Morphological analysis on data derived from the newly developed Wits Face Database [41,72] using the FISWG feature list [47] was found highly accurate and reliable when comparing optimal standardized photographs to wildtype (informal) unstandardized photographs [21].”
From Forensic Facial Comparison: Current Status, Limitations, and Future Directions (2021) · cites this paper as [21]
Forensic facial comparison for identification remains largely untested, despite its increasing demand.
“However, forensic facial comparison (FFC) for identification remains largely untested, despite its increasing demand [17,21].”
From Forensic Facial Comparison: Current Status, Limitations, and Future Directions (2021) · cites this paper as [21]
In an analysis of 75 face sets (n = 750 comparisons), chance-corrected accuracy and reliability of MA on optimal photographs were 99.1% and 92.1%, respectively.
“In an analysis of 75 sets of faces (each containing nine no match comparisons and one positive match comparison or 10 no match comparisons—compared to a target image, total n = 750 comparisons), the chance corrected accuracy and reliability were found to be almost perfect in optimal photographs (99.1% and 92.1%, respectively) [21].”
From Forensic Facial Comparison: Current Status, Limitations, and Future Directions (2021) · cites this paper as [21]
Eye-level digital CCTV recording-based facial comparisons were almost as accurate (97.3%) and reliable (77.3%) as standardized photograph to unstandardized photograph comparisons.
“In fact, eye-level digital CCTV recording-based facial comparisons were almost as accurate (97.3%) and reliable (77.3%) as the standardized photograph to unstandardized photograph comparisons [21,48] (Figure 2).”
From Forensic Facial Comparison: Current Status, Limitations, and Future Directions (2021) · cites this paper as [21]
All other analyses from the various CCTV and photographic images showed improved performance under a strict iteration.
“All other analyses from the various CCTV and photographic images showed improved performance under a strict iteration [21,48,49].”
From Forensic Facial Comparison: Current Status, Limitations, and Future Directions (2021) · cites this paper as [21]
Faces in the standard CCTV were composed of approximately double the number of pixels (41 × 52 pixels) and a much higher accuracy and reliability were obtained.
“This is a suggested preliminary lower threshold as despite conditions being mostly similar between standard CCTV and analogue CCTV, in terms of angle of incidence and SCD, faces in the standard CCTV were composed of approximately double the number of pixels (41 × 52 pixels) and a much higher accuracy and reliability were obtained [21].”
From Forensic Facial Comparison: Current Status, Limitations, and Future Directions (2021) · cites this paper as [21]
Over- and under-exposed footage may render an analysis impossible, based on multiple facial features being completely unrecognizable.
“Over- and under-exposed footage may render an analysis impossible, based on multiple facial features being completely unrecognizable [21,106].”
From Forensic Facial Comparison: Current Status, Limitations, and Future Directions (2021) · cites this paper as [21]
The conditions in the studies were limited to the major questions broadly investigated by each study.
“In doing so, although realistic, the conditions were limited to the major questions broadly investigated by each study [21,48,49].”
From Forensic Facial Comparison: Current Status, Limitations, and Future Directions (2021) · cites this paper as [21]
Only two particular CCTV camera specifications under limited conditions and installation variations were tested in our results.
“In consideration of our results, only two particular CCTV camera specifications under limited conditions and installation variations were tested [21,48,49].”
From Forensic Facial Comparison: Current Status, Limitations, and Future Directions (2021) · cites this paper as [21]
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Metadata
- Authors
- Bacci, N., Houlton, T. M. R., Briers, N., & Steyn, M.
- Affiliations
- University of the Witwatersrand (ZA)
- Corresponding author
- Bacci, Nicholas
- Venue
- International Journal of Legal Medicine
- Published
- 2021-02-16
- Volume
- 135
- Issue
- 5
- Pages
- 1965-1981
- ISSN
- 0044-3433 (ISSN-L)
- 0937-9827
- 1437-1596
- OpenAlex
- W3131498708
- Cited by
- 23
- References
- 53
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 7 · 2023: 4 · 2022: 3 · 2021: 4
- FWCI
- 1.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Legal Medicine (publishedVersion)
- PubMed (publishedVersion)
- Discovery Research Portal (University of Dundee) (submittedVersion)
- Funders
- South African National Research Foundation (11858, CSUR160425163022; UID: 106031)
- J.J.J. Smieszeck Fellowship (N/A)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- morphological analysis, CCTV footage, face identification, image quality, accuracy and reliability, false negative errors
- MeSH terms
- Facial Recognition, Adult, Humans, Male, Photography, Predictive Value of Tests, Sensitivity and Specificity, South Africa, Television, Video Recording, Databases, Factual, Guidelines as Topic, Forensic Sciences
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