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Fysh MC, Bindemann M. Understanding face matching. Q J Exp Psychol (Hove) 2023; 76:862-880. [PMID: 35587796 PMCID: PMC10031636 DOI: 10.1177/17470218221104476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Many security settings rely on the identity matching of unfamiliar people, which has led this task to be studied extensively in Cognitive Psychology. In these experiments, observers typically decide whether pairs of faces depict one person (an identity match) or two different people (an identity mismatch). The visual similarity of the to-be-compared faces must play a primary role in how observers accurately resolve this task, but the nature of this similarity-accuracy relationship is unclear. The current study investigated the association between accuracy and facial similarity at the level of individual items (Experiments 1 and 2) and facial features (Experiments 3 and 4). All experiments demonstrate a strong link between similarity and matching accuracy, indicating that this forms the basis of identification decisions. At a feature level, however, similarity exhibited distinct relationships with match and mismatch accuracy. In matches, similarity information was generally shared across the features of a face pair under comparison, with greater similarity linked to higher accuracy. Conversely, features within mismatching face pairs exhibited greater variation in similarity information. This indicates that identity matches and mismatches are characterised by different similarity profiles, which present distinct challenges to the cognitive system. We propose that these identification decisions can be resolved through the accumulation of convergent featural information in matches and the evaluation of divergent featural information in mismatches.
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Affiliation(s)
- Matthew C Fysh
- School of Psychology, University of Kent, Canterbury, UK
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2
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DepTSol: An Improved Deep-Learning- and Time-of-Flight-Based Real-Time Social Distance Monitoring Approach under Various Low-Light Conditions. ELECTRONICS 2022. [DOI: 10.3390/electronics11030458] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Social distancing is an utmost reliable practice to minimise the spread of coronavirus disease (COVID-19). As the new variant of COVID-19 is emerging, healthcare organisations are concerned with controlling the death and infection rates. Different COVID-19 vaccines have been developed and administered worldwide. However, presently developed vaccine quantity is not sufficient to fulfil the needs of the world’s population. The precautionary measures still rely on personal preventive strategies. The sharp rise in infections has forced governments to reimpose restrictions. Governments are forcing people to maintain at least 6 feet (ft) of safe physical distance to stay safe. With summers, low-light conditions can become challenging. Especially in the cities of underdeveloped countries, where poor ventilated and congested homes cause people to gather in open spaces such as parks, streets, and markets. Besides this, in summer, large friends and family gatherings mostly take place at night. It is necessary to take precautionary measures to avoid more drastic results in such situations. To support the law and order bodies in maintaining social distancing using Social Internet of Things (SIoT), the world is considering automated systems. To address the identification of violations of a social distancing Standard Operating procedure (SOP) in low-light environments via smart, automated cyber-physical solutions, we propose an effective social distance monitoring approach named DepTSol. We propose a low-cost and easy-to-maintain motionless monocular time-of-flight (ToF) camera and deep-learning-based object detection algorithms for real-time social distance monitoring. The proposed approach detects people in low-light environments and calculates their distance in terms of pixels. We convert the predicted pixel distance into real-world units and compare it with the specified safety threshold value. The system highlights people violating the safe distance. The proposed technique is evaluated by COCO evaluation metrics and has achieved a good speed–accuracy trade-off with 51.2 frames per second (fps) and a 99.7% mean average precision (mAP) score. Besides the provision of an effective social distance monitoring approach, we perform a comparative analysis between one-stage object detectors and evaluate their performance in low-light environments. This evaluation will pave the way for researchers to study the field further and will enlighten the efficiency of deep-learning algorithms in timely responsive real-world applications.
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3
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Agarwal A, Singh R, Vatsa M, Noore A. MagNet: Detecting Digital Presentation Attacks on Face Recognition. Front Artif Intell 2021; 4:643424. [PMID: 34957389 PMCID: PMC8692743 DOI: 10.3389/frai.2021.643424] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 08/26/2021] [Indexed: 11/13/2022] Open
Abstract
Presentation attacks on face recognition systems are classified into two categories: physical and digital. While much research has focused on physical attacks such as photo, replay, and mask attacks, digital attacks such as morphing have received limited attention. With the advancements in deep learning and computer vision algorithms, several easy-to-use applications are available where with few taps/clicks, an image can be easily and seamlessly altered. Moreover, generation of synthetic images or modifying images/videos (e.g. creating deepfakes) is relatively easy and highly effective due to the tremendous improvement in generative machine learning models. Many of these techniques can be used to attack the face recognition systems. To address this potential security risk, in this research, we present a novel algorithm for digital presentation attack detection, termed as MagNet, using a "Weighted Local Magnitude Pattern" (WLMP) feature descriptor. We also present a database, termed as ID Age nder, which consists of three different subsets of swapping/morphing and neural face transformation. In contrast to existing research, which utilizes sophisticated machine learning networks for attack generation, the databases in this research are prepared using social media platforms that are readily available to everyone with and without any malicious intent. Experiments on the proposed database, FaceForensic database, GAN generated images, and real-world images/videos show the stimulating performance of the proposed algorithm. Through the extensive experiments, it is observed that the proposed algorithm not only yields lower error rates, but also provides computational efficiency.
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Affiliation(s)
- Akshay Agarwal
- Indraprastha Institute of Information Technology Delhi, New Delhi, India
| | - Richa Singh
- Indian Institute of Technology Jodhpur, Jodhpur, India
| | - Mayank Vatsa
- Indian Institute of Technology Jodhpur, Jodhpur, India
| | - Afzel Noore
- Texas A&M University, Kingsville, TX, United States
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4
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Batskos I, Wit FF, Spreeuwers LJ, Veldhuis RJ. Preventing face morphing attacks by using legacy face images. IET BIOMETRICS 2021. [DOI: 10.1049/bme2.12047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Affiliation(s)
- Ilias Batskos
- Department of Computer Science Data Management & Biometrics group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede The Netherlands
| | - Florens F. Wit
- Department of Computer Science Data Management & Biometrics group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede The Netherlands
| | - Luuk J. Spreeuwers
- Department of Computer Science Data Management & Biometrics group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede The Netherlands
| | - Raymond J. Veldhuis
- Department of Computer Science Data Management & Biometrics group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede The Netherlands
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5
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Abstract
A relatively new type of identity theft uses morphed facial images in identification documents in which images of two individuals are digitally blended to create an image that maintains a likeness to each of the original identities. We created a set of high-quality digital morphs from passport-style photos for a diverse set of people across gender, race, and age. We then examine people's ability to detect facial morphing both in terms of determining if two side-by-side faces are of the same individual or not and in terms of identifying if a face is the result of digital morphing. We show that human participants struggle at both tasks. Even modern machine-learning-based facial recognition struggles to distinguish between an individual and their morphed version. We conclude with a hopeful note, describing a computational technique that holds some promise in recognizing that one facial image is a morphed version of another.
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Affiliation(s)
| | - Shruti Agarwal
- Electrical Engineering & Computer Sciences, University of California, Berkeley, CA, USA.,
| | - Hany Farid
- Electrical Engineering & Computer Sciences and School of Information, University of California, Berkeley, CA, USA.,
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6
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Face Morphing, a Modern Threat to Border Security: Recent Advances and Open Challenges. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11073207] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Face morphing poses a serious threat to Automatic Border Control (ABC) and Face Recognition Systems (FRS) in general. The aim of this paper is to present a qualitative assessment of the morphing attack issue, and the challenges it entails, highlighting both the technological and human aspects of the problem. Here, after the face morphing attack scenario is presented, the paper provides an overview of the relevant bibliography and recent advances towards two central directions. First, the morphing of face images is outlined with a particular focus on the three main steps that are involved in the process, namely, landmark detection, face alignment and blending. Second, the detection of morphing attacks is presented under the prism of the so-called on-line and off-line detection scenarios and whether the proposed techniques employ handcrafted features, using classical methods, or automatically generated features, using deep-learning-based methods. The paper, then, presents the evaluation metrics that are employed in the corresponding bibliography and concludes with a discussion on open challenges that need to be address for further advancing automatic detection of morphing attacks. Despite the progress being made, the general consensus of the research community is that significant effort and resources are needed in the near future for the mitigation of the issue, especially, towards the creation of datasets capturing the full extent of the problem at hand and the availability of reference evaluation procedures for comparing novel automatic attack detection algorithms.
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7
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Rathgeb C, Bernardo K, Haryanto NE, Busch C. Effects of image compression on face image manipulation detection: A case study on facial retouching. IET BIOMETRICS 2021. [DOI: 10.1049/bme2.12027] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Affiliation(s)
- Christian Rathgeb
- da/sec—Biometrics and Internet Security Research Group Hochschule Darmstadt Darmstadt Germany
| | - Kevin Bernardo
- da/sec—Biometrics and Internet Security Research Group Hochschule Darmstadt Darmstadt Germany
| | - Nathania E. Haryanto
- da/sec—Biometrics and Internet Security Research Group Hochschule Darmstadt Darmstadt Germany
| | - Christoph Busch
- da/sec—Biometrics and Internet Security Research Group Hochschule Darmstadt Darmstadt Germany
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Robertson DJ, Sanders JG, Towler A, Kramer RSS, Spowage J, Byrne A, Burton AM, Jenkins R. Hyper-realistic Face Masks in a Live Passport-Checking Task. Perception 2020; 49:298-309. [PMID: 32013720 PMCID: PMC7583446 DOI: 10.1177/0301006620904614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Hyper-realistic face masks have been used as disguises in at least one border
crossing and in numerous criminal cases. Experimental tests using these masks
have shown that viewers accept them as real faces under a range of conditions.
Here, we tested mask detection in a live identity verification task. Fifty-four
visitors at the London Science Museum viewed a mask wearer at close range (2 m)
as part of a mock passport check. They then answered a series of questions
designed to assess mask detection, while the masked traveller was still in view.
In the identity matching task, 8% of viewers accepted the mask as matching a
real photo of someone else, and 82% accepted the match between masked person and
masked photo. When asked if there was any reason to detain the traveller, only
13% of viewers mentioned a mask. A further 11% picked disguise from a list of
suggested reasons. Even after reading about mask-related fraud, 10% of viewers
judged that the traveller was not wearing a mask. Overall, mask detection was
poor and was not predicted by unfamiliar face matching performance. We conclude
that hyper-realistic face masks could go undetected during live identity
checks.
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Affiliation(s)
- David J. Robertson
- Department of Psychology, University of York, UK; School of Psychological Sciences and Health, University of Strathclyde, UK
- Department of Psychology, University of York, UK
| | - Jet G. Sanders
- Department of Psychology, University of York, UK; Department of Psychology and Behavioural Sciences, London School of Economics and Political Science, UK
- Department of Psychology, University of York, UK
| | - Alice Towler
- Department of Psychology, University of York, UK; School of Psychology, University of New South Wales, Australia
- Department of Psychology, University of York, UK
| | - Robin S. S. Kramer
- Department of Psychology, University of York, UK; School of Psychology, University of Lincoln, UK
- Department of Psychology, University of York, UK
| | - Josh Spowage
- Department of Psychology, University of York, UK; Division of Psychology and Language Sciences, University College London, UK
- Department of Psychology, University of York, UK
| | - Ailish Byrne
- Department of Psychology, University of York, UK; Department of Psychology, Edge Hill University, UK
- Department of Psychology, University of York, UK
| | | | - Rob Jenkins
- Department of Psychology, University of York, UK
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9
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Heyer R, Chong C, Semmler C. Facial image comparisons of morphed facial imagery. AUST J FORENSIC SCI 2019. [DOI: 10.1080/00450618.2019.1571106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Rebecca Heyer
- National Security and Intelligence, Surveillance and Reconnaissance Division, Defence Science and Technology Group, Edinburgh, Australia
| | - Celine Chong
- School of Psychology, University of Adelaide, Adelaide, Australia
| | - Carolyn Semmler
- School of Psychology, University of Adelaide, Adelaide, Australia
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10
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Kramer RSS, Mireku MO, Flack TR, Ritchie KL. Face morphing attacks: Investigating detection with humans and computers. COGNITIVE RESEARCH-PRINCIPLES AND IMPLICATIONS 2019; 4:28. [PMID: 31359213 PMCID: PMC6663958 DOI: 10.1186/s41235-019-0181-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Accepted: 06/21/2019] [Indexed: 11/21/2022]
Abstract
Background In recent years, fraudsters have begun to use readily accessible digital manipulation techniques in order to carry out face morphing attacks. By submitting a morph image (a 50/50 average of two people’s faces) for inclusion in an official document such as a passport, it might be possible that both people sufficiently resemble the morph that they are each able to use the resulting genuine ID document. Limited research with low-quality morphs has shown that human detection rates were poor but that training methods can improve performance. Here, we investigate human and computer performance with high-quality morphs, comparable with those expected to be used by criminals. Results Over four experiments, we found that people were highly error-prone when detecting morphs and that training did not produce improvements. In a live matching task, morphs were accepted at levels suggesting they represent a significant concern for security agencies and detection was again error-prone. Finally, we found that a simple computer model outperformed our human participants. Conclusions Taken together, these results reinforce the idea that advanced computational techniques could prove more reliable than training people when fighting these types of morphing attacks. Our findings have important implications for security authorities worldwide. Electronic supplementary material The online version of this article (10.1186/s41235-019-0181-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Robin S S Kramer
- School of Psychology, University of Lincoln, Lincoln, LN6 7TS, UK.
| | - Michael O Mireku
- School of Psychology, University of Lincoln, Lincoln, LN6 7TS, UK
| | - Tessa R Flack
- School of Psychology, University of Lincoln, Lincoln, LN6 7TS, UK
| | - Kay L Ritchie
- School of Psychology, University of Lincoln, Lincoln, LN6 7TS, UK
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11
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Robertson DJ, Mungall A, Watson DG, Wade KA, Nightingale SJ, Butler S. Detecting morphed passport photos: a training and individual differences approach. Cogn Res Princ Implic 2018; 3:27. [PMID: 30046650 PMCID: PMC6028877 DOI: 10.1186/s41235-018-0113-8] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2017] [Accepted: 05/08/2018] [Indexed: 11/10/2022] Open
Abstract
Our reliance on face photos for identity verification is at odds with extensive research which shows that matching pairs of unfamiliar faces is highly prone to error. This process can therefore be exploited by identity fraudsters seeking to deceive ID checkers (e.g., using a stolen passport which contains an image of a similar looking individual to deceive border control officials). In this study we build on previous work which sought to quantify the threat posed by a relatively new type of fraud: morphed passport photos. Participants were initially unaware of the presence of morphs in a series of face photo arrays and were simply asked to detect which images they thought had been digitally manipulated (i.e., "images that didn't look quite right"). All participants then received basic information on morph fraud and rudimentary guidance on how to detect such images, followed by a morph detection training task (Training Group, n = 40), or a non-face control task (Guidance Group, n = 40). Participants also completed a post-guidance/training morph detection task and the Models Face Matching Test (MFMT). Our findings show that baseline morph detection rates were poor, that morph detection training significantly improved the identification of these images over and above basic guidance, and that accuracy in the mismatch condition of the MFMT correlated with morph detection ability. The results are discussed in relation to potential countermeasures for morph-based identity fraud.
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Affiliation(s)
- David J. Robertson
- School of Psychological Sciences and Health, University of Strathclyde, Glasgow, G1 1QE UK
| | - Andrew Mungall
- School of Psychological Sciences and Health, University of Strathclyde, Glasgow, G1 1QE UK
| | | | | | | | - Stephen Butler
- School of Psychological Sciences and Health, University of Strathclyde, Glasgow, G1 1QE UK
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12
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Neubert T, Makrushin A, Hildebrandt M, Kraetzer C, Dittmann J. Extended
StirTrace
benchmarking of biometric and forensic qualities of morphed face images. IET BIOMETRICS 2018. [DOI: 10.1049/iet-bmt.2017.0147] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Affiliation(s)
- Tom Neubert
- Department of Computer ScienceOtto‐von‐Guericke‐University Magdeburg, Research Group Multimedia and SecurityP.O. Box 412039016MagdeburgGermany
| | - Andrey Makrushin
- Department of Computer ScienceOtto‐von‐Guericke‐University Magdeburg, Research Group Multimedia and SecurityP.O. Box 412039016MagdeburgGermany
| | - Mario Hildebrandt
- Department of Computer ScienceOtto‐von‐Guericke‐University Magdeburg, Research Group Multimedia and SecurityP.O. Box 412039016MagdeburgGermany
| | - Christian Kraetzer
- Department of Computer ScienceOtto‐von‐Guericke‐University Magdeburg, Research Group Multimedia and SecurityP.O. Box 412039016MagdeburgGermany
| | - Jana Dittmann
- Department of Computer ScienceOtto‐von‐Guericke‐University Magdeburg, Research Group Multimedia and SecurityP.O. Box 412039016MagdeburgGermany
- Department of Applied ComputingUniversity of Buckingham, School of ScienceBuckinghamMK18 1EGUK
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13
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Sanders JG, Ueda Y, Minemoto K, Noyes E, Yoshikawa S, Jenkins R. Hyper-realistic face masks: a new challenge in person identification. COGNITIVE RESEARCH-PRINCIPLES AND IMPLICATIONS 2017; 2:43. [PMID: 29104914 PMCID: PMC5655619 DOI: 10.1186/s41235-017-0079-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2017] [Accepted: 09/25/2017] [Indexed: 11/29/2022]
Abstract
We often identify people using face images. This is true in occupational settings such as passport control as well as in everyday social environments. Mapping between images and identities assumes that facial appearance is stable within certain bounds. For example, a person’s apparent age, gender and ethnicity change slowly, if at all. It also assumes that deliberate changes beyond these bounds (i.e., disguises) would be easy to spot. Hyper-realistic face masks overturn these assumptions by allowing the wearer to look like an entirely different person. If unnoticed, these masks break the link between facial appearance and personal identity, with clear implications for applied face recognition. However, to date, no one has assessed the realism of these masks, or specified conditions under which they may be accepted as real faces. Herein, we examined incidental detection of unexpected but attended hyper-realistic masks in both photographic and live presentations. Experiment 1 (UK; n = 60) revealed no evidence for overt detection of hyper-realistic masks among real face photos, and little evidence of covert detection. Experiment 2 (Japan; n = 60) extended these findings to different masks, mask-wearers and participant pools. In Experiment 3 (UK and Japan; n = 407), passers-by failed to notice that a live confederate was wearing a hyper-realistic mask and showed limited evidence of covert detection, even at close viewing distance (5 vs. 20 m). Across all of these studies, viewers accepted hyper-realistic masks as real faces. Specific countermeasures will be required if detection rates are to be improved.
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Affiliation(s)
| | | | | | - Eilidh Noyes
- Department of Psychology, University of York, Heslington, York, YO10 5DD UK
| | | | - Rob Jenkins
- Department of Psychology, University of York, Heslington, York, YO10 5DD UK
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