Face averages enhance user recognition for smartphone security
Our recognition of familiar faces is excellent, and generalises across viewing conditions. However, unfamiliar face recognition is much poorer. For this reason, automatic face recognition systems might benefit from incorporating the advantages of familiarity. Here we put this to the test using the f...
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| Veröffentlicht in: | PloS one 2015-03, Vol.10 (3), p.e0119460-e0119460 |
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| description | Our recognition of familiar faces is excellent, and generalises across viewing conditions. However, unfamiliar face recognition is much poorer. For this reason, automatic face recognition systems might benefit from incorporating the advantages of familiarity. Here we put this to the test using the face verification system available on a popular smartphone (the Samsung Galaxy). In two experiments we tested the recognition performance of the smartphone when it was encoded with an individual's 'face-average'--a representation derived from theories of human face perception. This technique significantly improved performance for both unconstrained celebrity images (Experiment 1) and for real faces (Experiment 2): users could unlock their phones more reliably when the device stored an average of the user's face than when they stored a single image. This advantage was consistent across a wide variety of everyday viewing conditions. Furthermore, the benefit did not reduce the rejection of imposter faces. This benefit is brought about solely by consideration of suitable representations for automatic face recognition, and we argue that this is just as important as development of matching algorithms themselves. We propose that this representation could significantly improve recognition rates in everyday settings. |
| doi_str_mv | 10.1371/journal.pone.0119460 |
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However, unfamiliar face recognition is much poorer. For this reason, automatic face recognition systems might benefit from incorporating the advantages of familiarity. Here we put this to the test using the face verification system available on a popular smartphone (the Samsung Galaxy). In two experiments we tested the recognition performance of the smartphone when it was encoded with an individual's 'face-average'--a representation derived from theories of human face perception. This technique significantly improved performance for both unconstrained celebrity images (Experiment 1) and for real faces (Experiment 2): users could unlock their phones more reliably when the device stored an average of the user's face than when they stored a single image. This advantage was consistent across a wide variety of everyday viewing conditions. Furthermore, the benefit did not reduce the rejection of imposter faces. This benefit is brought about solely by consideration of suitable representations for automatic face recognition, and we argue that this is just as important as development of matching algorithms themselves. We propose that this representation could significantly improve recognition rates in everyday settings.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0119460</identifier><identifier>PMID: 25807251</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adult ; Algorithms ; Celebrities ; Coding ; Cognition & reasoning ; Computer Security ; Event driven simulation ; Face ; Face recognition ; Familiarity ; Female ; Galaxies ; Humans ; Male ; Middle Aged ; Pattern recognition ; Pattern Recognition, Visual ; Recognition (Psychology) ; Representations ; Security ; Smartphone ; Smartphones ; Tablet computers ; Viewing ; Young Adult</subject><ispartof>PloS one, 2015-03, Vol.10 (3), p.e0119460-e0119460</ispartof><rights>2015 Robertson et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2015 Robertson et al 2015 Robertson et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c526t-f44f625500ababcd041645bdf829a64c1431281301cc3f6bedb84b957ede56f53</citedby><cites>FETCH-LOGICAL-c526t-f44f625500ababcd041645bdf829a64c1431281301cc3f6bedb84b957ede56f53</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4373928/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4373928/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,725,778,782,862,883,2098,2917,23853,27911,27912,53778,53780,79355,79356</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25807251$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Luo, Wenbo</contributor><creatorcontrib>Robertson, David J</creatorcontrib><creatorcontrib>Kramer, Robin S S</creatorcontrib><creatorcontrib>Burton, A Mike</creatorcontrib><title>Face averages enhance user recognition for smartphone security</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Our recognition of familiar faces is excellent, and generalises across viewing conditions. However, unfamiliar face recognition is much poorer. For this reason, automatic face recognition systems might benefit from incorporating the advantages of familiarity. Here we put this to the test using the face verification system available on a popular smartphone (the Samsung Galaxy). In two experiments we tested the recognition performance of the smartphone when it was encoded with an individual's 'face-average'--a representation derived from theories of human face perception. This technique significantly improved performance for both unconstrained celebrity images (Experiment 1) and for real faces (Experiment 2): users could unlock their phones more reliably when the device stored an average of the user's face than when they stored a single image. This advantage was consistent across a wide variety of everyday viewing conditions. Furthermore, the benefit did not reduce the rejection of imposter faces. This benefit is brought about solely by consideration of suitable representations for automatic face recognition, and we argue that this is just as important as development of matching algorithms themselves. We propose that this representation could significantly improve recognition rates in everyday settings.</description><subject>Adult</subject><subject>Algorithms</subject><subject>Celebrities</subject><subject>Coding</subject><subject>Cognition & reasoning</subject><subject>Computer Security</subject><subject>Event driven simulation</subject><subject>Face</subject><subject>Face recognition</subject><subject>Familiarity</subject><subject>Female</subject><subject>Galaxies</subject><subject>Humans</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Pattern recognition</subject><subject>Pattern Recognition, Visual</subject><subject>Recognition (Psychology)</subject><subject>Representations</subject><subject>Security</subject><subject>Smartphone</subject><subject>Smartphones</subject><subject>Tablet computers</subject><subject>Viewing</subject><subject>Young 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However, unfamiliar face recognition is much poorer. For this reason, automatic face recognition systems might benefit from incorporating the advantages of familiarity. Here we put this to the test using the face verification system available on a popular smartphone (the Samsung Galaxy). In two experiments we tested the recognition performance of the smartphone when it was encoded with an individual's 'face-average'--a representation derived from theories of human face perception. This technique significantly improved performance for both unconstrained celebrity images (Experiment 1) and for real faces (Experiment 2): users could unlock their phones more reliably when the device stored an average of the user's face than when they stored a single image. This advantage was consistent across a wide variety of everyday viewing conditions. Furthermore, the benefit did not reduce the rejection of imposter faces. 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| subjects | Adult Algorithms Celebrities Coding Cognition & reasoning Computer Security Event driven simulation Face Face recognition Familiarity Female Galaxies Humans Male Middle Aged Pattern recognition Pattern Recognition, Visual Recognition (Psychology) Representations Security Smartphone Smartphones Tablet computers Viewing Young Adult |
| title | Face averages enhance user recognition for smartphone security |
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