Matthew C. Fysh

Face matching in a long task: enforced rest and desk-switching cannot maintain identification accuracy

In face matching, observers have to decide whether two photographs depict the same person or different people. This task is not only remarkably difficult but accuracy declines further during prolonged testing. The current study investigated whether this decline in long tasks can be eliminated with regular rest-breaks (Experiment 1) or room-switching (Experiment 2). Both experiments replicated the accuracy decline for long face-matching tasks and showed that this could not be eliminated with rest or room-switching. These findings suggest that person identification in applied settings, such as passport control, might be particularly error-prone due to the long and repetitive nature of the task. The experiments also show that it is difficult to counteract these problems.

The Kent Face Matching Test

This study presents the Kent Face Matching Test (KFMT), which comprises 200 same-identity and 20 different-identity pairs of unfamiliar faces. Each face pair consists of a photograph from a student ID card and a high-quality portrait that was taken at least three months later. The test is designed to complement existing resources for face-matching research, by providing a more ecologically valid stimulus set that captures the natural variability that can arise in a persons appearance over time. Two experiments are presented to demonstrate that the KFMT provides a challenging measure of face matching but correlates with established tests. Experiment 1 compares a short version of this test with the optimized Glasgow Face Matching Test (GFMT). In Experiment 2, a longer version of the KFMT, with infrequent identity mismatches, is correlated with performance on the Cambridge Face Memory Test (CFMT) and the Cambridge Face Perception Test (CFPT). The KFMT is freely available for use in face-matching research.

Matching Faces Against the Clock

This study examined the effect of time pressure on face-matching accuracy. Across two experiments, observers decided whether pairs of faces depict one person or different people. Time pressure was exerted via two additional displays, which were constantly updated to inform observers on whether they were on track to meet or miss a time target. In this paradigm, faces were matched under increasing or decreasing (Experiment 1) and constant time pressure (Experiment 2), which varied from 10 to 2 seconds. In both experiments, time pressure reduced accuracy, but the point at which this declined varied from 8 to 2 seconds. A separate match response bias was found, which developed over the course of the experiments. These results indicate that both time pressure and the repetitive nature of face matching are detrimental to performance.

Effects of time pressure and time passage on face-matching accuracy

This study investigated the impact of time pressure on matching accuracy with face pairs that combined photographs from student ID cards with high-quality person portraits, and under conditions that provided infrequent identity mismatches. Time pressure was administered via two onscreen displays that observers could use to adjust the amount of time that was allocated to a given trial while completing a block of trials within a required timeframe. Under these conditions, observers matched faces under time pressure that varied from 10 to 2 s (Experiment 1) and 8 to 2 s (Experiment 2). An effect of time pressure was found in each experiment, whereby performance deteriorated under time targets of 4 s. Additionally, a match response bias emerged consistently across blocks, and indicated that separately to time pressure, performance also deteriorated due to time passage. These results therefore indicate that both time passage and pressure exert detrimental effects on face matching.

Individual differences in the detection, matching and memory of faces

Previous research has explored relationships between individual performance in the detection, matching and memory of faces, but under limiting conditions. The current study sought to extend previous findings with a different measure of face detection, and a more challenging face matching task, in combination with an established test of face memory. Experiment 1 tested face detection ability under conditions designed to maximise individual differences in accuracy but did not find evidence for relationships between measures. In addition, in Experiments 2 and 3, which utilised response times as the primary performance measure for face detection, but accuracy for face matching and face memory, no correlations were observed between performance on face detection and the other tasks. However, there was a correlation between accuracy in face matching and face memory, consistent with other research. Together, these experiments provide further evidence for a dissociation between face detection, and face matching and face memory, but suggest that these latter tasks share some common mechanisms.

Human-computer interaction in face matching

Abstract Automatic facial recognition is becoming increasingly ubiquitous in security contexts such as passport control. Currently, Automated Border Crossing (ABC) systems in the United Kingdom (UK) and the European Union (EU) require supervision from a human operator who validates correct identity judgments and overrules incorrect decisions. As the accuracy of this human–computer interaction remains unknown, this research investigated how human validation is impacted by a priori face‐matching decisions such as those made by automated face recognition software. Observers matched pairs of faces that were already labeled onscreen as depicting the same identity or two different identities. The majority of these labels provided information that was consistent with the stimuli presented, but some were also inconsistent or provided “unresolved” information. Across three experiments, accuracy consistently deteriorated on trials that were inconsistently labeled, indicating that observers’ face‐matching decisions are biased by external information such as that provided by ABCs.

Person identification from aerial footage by a remote-controlled drone

Remote-controlled aerial drones (or unmanned aerial vehicles; UAVs) are employed for surveillance by the military and police, which suggests that drone-captured footage might provide sufficient information for person identification. This study demonstrates that person identification from drone-captured images is poor when targets are unfamiliar (Experiment 1), when targets are familiar and the number of possible identities is restricted by context (Experiment 2), and when moving footage is employed (Experiment 3). Person information such as sex, race and age is also difficult to access from drone-captured footage (Experiment 4). These findings suggest that such footage provides a particularly poor medium for person identification. This is likely to reflect the sub-optimal quality of such footage, which is subject to factors such as the height and velocity at which drones fly, viewing distance, unfavourable vantage points, and ambient conditions.

Correction to ‘Effects of time pressure and time passage on face-matching accuracy’

[This corrects the article DOI: 10.1098/rsos.170249.].