Face search in CCTV surveillance

Search efforts and face recognition: the role of expectations of encounter and within-person variability in prospective person memory

People perform poorly at sighting missing and wanted persons in simulated searches due to attention and face recognition failures. We manipulated participants' expectations of encountering a target person and the within-person variability of the targets' photographs studied in a laboratory-based and a field-based prospective person memory task. We hypothesized that within-person variability and expectations of encounter would impact prospective person memory performance, and that expectations would interact with within-person variability to mitigate the effect of variability. Surprisingly, low within-person variability resulted in better performance on the search task than high within-person variability in Experiment one possibly due to the study-test images being rated as more similar in the low variability condition. We found the expected effect of high variability producing more hits for the target whose study-test images were equally similar across variability conditions. There was no effect of variability in Experiment two. Expectations affected performance only in the field-based study (Experiment two), possibly because performance is typically poor in field-based studies. Our research demonstrates some nuance to the effect of within-person variability on search performance and extends existing research demonstrating expectations affect search performance.

Prioritising quality: investigating the influence of image quality on forensic facial comparison

Morphological analysis in forensic facial comparison was recently validated for judicial use. However, no image quality assurance systems exist for this purpose, despite image triage being considered the best practice approach. Hence, this study aimed at testing a semi-quantitative scoring method to assess image quality and investigated facial image resolution and lighting quality quantitatively in a context of forensic facial comparison. For this purpose, 400 facial comparison photographic and CCTV image pools developed from the Wits Face Database were used. These facial images were analysed in prior studies that investigated the validity of morphological analysis. A semi-quantitative image quality scoring system was adapted and tested on the above sample and compared across correct and incorrect matches obtained as part of previous studies using a logistic regression model. In addition, facial images were cropped to the closest pixel comprising the face, head and neck areas; then, a face-to-image pixel proportion was calculated as an estimator of resolution quality; and pixel exposure qualities were obtained to be compared to facial comparison outcomes. Ideal and high image quality scores were related to correctness of matches, while low-quality scores were related to incorrect matches. High pixel proportions were related to true matches and low exposure was related to false positives, while high exposure was related to false negatives. These results suggest that an easy method for image triage could be employed by scoring image quality. Quantitative measures should be investigated further for thresholding quality suitability for confidence of facial comparisons.

Provoked overt recognition in acquired prosopagnosia using multiple different images of famous faces

Provoked overt recognition refers to the fact that patients with acquired prosopagnosia can sometimes recognize faces when presented in arrays of individuals from the same category (e.g., actors or politicians). We ask whether a prosopagnosic patient might experience recognition when presented with multiple different images of the same face simultaneously. Over two sessions, patient Herschel, a 66-year-old British man with acquired prosopagnosia, viewed face images individually or in arrays. On several occasions he failed to recognize single photos of an individual but successfully identified that person when the same photos were presented together. For example, Herschel failed to recognize any individual images of King Charles or Paul McCartney but recognised both in arrays of the same photos. Like reports based on category membership, overt recognition was transient and inconsistent. These findings are discussed in terms of models of covert recognition, alongside more recent research on within-person variability for face perception.

Forensic Facial Comparison: Current Status, Limitations, and Future Directions

Global escalation of crime has necessitated the use of digital imagery to aid the identification of perpetrators. Forensic facial comparison (FFC) is increasingly employed, often relying on poor-quality images. In the absence of standardized criteria, especially in terms of video recordings, verification of the methodology is needed. This paper addresses aspects of FFC, discussing relevant terminology, investigating the validity and reliability of the FISWG morphological feature list using a new South African database, and advising on standards for CCTV equipment. Suboptimal conditions, including poor resolution, unfavorable angle of incidence, color, and lighting, affected the accuracy of FFC. Morphological analysis of photographs, standard CCTV, and eye-level CCTV showed improved performance in a strict iteration analysis, but not when using analogue CCTV images. Therefore, both strict and lenient iterations should be conducted, but FFC must be abandoned when a strict iteration performs worse than a lenient one. This threshold ought to be applied to the specific CCTV equipment to determine its utility. Chance-corrected accuracy was the most representative measure of accuracy, as opposed to the commonly used hit rate. While the use of automated systems is increasing, trained human observer-based morphological analysis, using the FISWG feature list and an Analysis, Comparison, Evaluation, and Verification (ACE-V) approach, should be the primary method of facial comparison.

Face processing in police service: the relationship between laboratory-based assessment of face processing abilities and performance in a real-world identity matching task

In the present study, we investigated whether police officers’ performance in searching for unfamiliar faces in a video-based real-world task is predicted by laboratory-based face processing tests that are typically used to assess individual differences in face processing abilities. Specifically, perceptual performance in the field was operationalized via the identification of target individuals in self-made close-circuit television (CCTV) video tapes. Police officers’ abilities in the laboratory were measured by the Cambridge Face Memory Test long form (CFMT+). We hypothesized that the CFMT+ predicts individual differences in the CCTV task performance. A total of N = 186 police officers of the Rhineland-Palatinate State Police participated in the study (i.e., N = 139 novice and advanced cadets with either 3 months, 15 months or 24 months of pre-service experience; N = 47 experienced police officers with three years of pre-service experience and at least two years of full-service experience, who participated in the assessment center of the special police forces, specifically the surveillance and technical unit). Results revealed that the CFMT+ explained variance in the CCTV task. In sample 1, CFMT+ scores predicted hits, but not false alarms. In contrast, in sample 2, CFMT+ scores were correlated with both hits and false alarms. From a theoretical perspective, we discuss factors that might explain CCTV task performance. From a practical perspective, we recommend that personnel selection processes investigating individual differences of police officers’ face processing abilities should comprise of two steps. At first, laboratory-based tests of face processing abilities should be applied. Subsequently, to validate laboratory-based individual differences in face processing abilities, we recommend that work samples such as CCTV tasks from the field should be added.

UIOT-FMT: A Universal Format for Collection and Aggregation of Data from Smart Devices

Information Technology (IT) has become an essential part of our lives and due to the emergence of the Internet-of-Things (IoT), technology has encompassed a majority of things that humans rely on in their daily lives. Furthermore, as IT becomes more relevant in daily lives, the need for IT to serve public emergency services has become more important. However, due to the infancy status of IoT, there is a need for a data consortium that would prove to be best used in servicing policing in a technological driven society. This paper will discuss the plausibility of creating a universal format for use in carrying out public services, such as emergency response by the police and regular law maintenance. In this research we will discuss what the police requires in their line-of-duty and how smart devices can be used to satisfy those needs. A data formatting framework is developed and demonstrated, with the goal of showing what can be done to unifying data from smart city sensors.