Supplemental Material for Diagnostic Feature Training Improves Face Matching Accuracy

Author(s):  
Alice Towler ◽  
Michelle Keshwa ◽  
Bianca Ton ◽  
Richard I. Kemp ◽  
David White

Perception ◽  
2018 ◽  
Vol 47 (4) ◽  
pp. 414-431 ◽  
Author(s):  
Robin S. S. Kramer ◽  
Michael G. Reynolds

Research has systematically examined how laboratory participants and real-world practitioners decide whether two face photographs show the same person or not using frontal images. In contrast, research has not examined face matching using profile images. In Experiment 1, we ask whether matching unfamiliar faces is easier with frontal compared with profile views. Participants completed the original, frontal version of the Glasgow Face Matching Test, and also an adapted version where all face pairs were presented in profile. There was no difference in performance across the two tasks, suggesting that both views were similarly useful for face matching. Experiments 2 and 3 examined whether matching unfamiliar faces is improved when both frontal and profile views are provided. We compared face matching accuracy when both a frontal and a profile image of each face were presented, with accuracy using each view alone. Surprisingly, we found no benefit when both views were presented together in either experiment. Overall, these results suggest that either frontal or profile views provide substantially overlapping information regarding identity or participants are unable to utilise both sources of information when making decisions. Each of these conclusions has important implications for face matching research and real-world identification development.


2017 ◽  
Vol 4 (6) ◽  
pp. 170249 ◽  
Author(s):  
Matthew C. Fysh ◽  
Markus Bindemann

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.


2013 ◽  
Vol 27 (6) ◽  
pp. 735-753 ◽  
Author(s):  
Hamood M. Alenezi ◽  
Markus Bindemann

Neuroreport ◽  
1999 ◽  
Vol 10 (9) ◽  
pp. 1965-1971 ◽  
Author(s):  
Gene E. Alexander ◽  
Marc J. Mentis ◽  
John D. Van Horn ◽  
Cheryl L. Grady ◽  
Karen F. Berman ◽  
...  

Cognition ◽  
2015 ◽  
Vol 141 ◽  
pp. 161-169 ◽  
Author(s):  
Kay L. Ritchie ◽  
Finlay G. Smith ◽  
Rob Jenkins ◽  
Markus Bindemann ◽  
David White ◽  
...  

2021 ◽  
Author(s):  
Daniel James Carragher ◽  
Alice Towler ◽  
Viktoria Roumenova Mileva ◽  
David White ◽  
Peter Hancock

To slow the spread of COVID-19, many people now wear face masks in public. Face masks impair our ability to identify faces, which can cause problems for professional staff who must identify offenders and members of the public. Here, we investigate whether performance on a masked face matching task can be improved by training participants to compare diagnostic facial features (the ears and facial marks) – a validated training method that improves matching performance for unmasked faces. We find strong evidence this brief diagnostic feature training, which takes less than two minutes to complete, improves matching performance for masked faces by approximately 5%. A control training course, which was unrelated to face identification, had no effect on matching performance. Our findings demonstrate that comparing the ears and facial marks is an effective means of improving face matching performance for masked faces. These findings have implications for professions that regularly perform face identification.


2018 ◽  
Vol 8 (9) ◽  
pp. 1561 ◽  
Author(s):  
Gi Nam ◽  
Heeseung Choi ◽  
Junghyun Cho ◽  
Ig-Jae Kim

Face recognition is one research area that has benefited from the recent popularity of deep learning, namely the convolutional neural network (CNN) model. Nevertheless, the recognition performance is still compromised by the model’s dependency on the scale of input images and the limited number of feature maps in each layer of the network. To circumvent these issues, we propose PSI-CNN, a generic pyramid-based scale-invariant CNN architecture which additionally extracts untrained feature maps across multiple image resolutions, thereby allowing the network to learn scale-independent information and improving the recognition performance on low resolution images. Experimental results on the LFW dataset and our own CCTV database show PSI-CNN consistently outperforming the widely-adopted VGG face model in terms of face matching accuracy.


PLoS ONE ◽  
2018 ◽  
Vol 13 (3) ◽  
pp. e0193455 ◽  
Author(s):  
Ahmed M. Megreya ◽  
Markus Bindemann

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