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

2017 ◽  
Vol 4 (9) ◽  
pp. 171159
Author(s):  
Matthew C. Fysh ◽  
Markus Bindemann
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.


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.


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 ◽  
...  

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

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

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

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4437 ◽  
Author(s):  
Ahmed M. Megreya

Identity comparisons of photographs of unfamiliar faces are prone to error but imperative for security settings, such as the verification of face identities at passport control. Therefore, finding techniques to improve face-matching accuracy is an important contemporary research topic. This study investigates whether matching accuracy can be enhanced by verbal instructions that address feature comparisons or holistic processing. Findings demonstrate that feature-by-feature comparison strategy had no effect on face matching. In contrast, verbal instructions focused on holistic processing made face matching faster, but they impaired accuracy. Given the recent evidence for the heredity of face perception and the previously reported small or no improvements of face-matching ability, it seems reasonable to suggest that improving unfamiliar face matching is not an easy task, but it is presumably worthwhile to explore new methods for improvement nonetheless.


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