Recognizing Faces

Young, Andrew W.; Burton, A. Mike

doi:10.1177/0963721416688114

Cited by 127 publications

(125 citation statements)

References 33 publications

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“…Finally, most traditional face recognition tasks use tightly controlled facial images that have been stripped of external features that could cue recognition (e.g., Bate, Haslam, Tree, & Hodgson, ; Duchaine & Nakayama, ; McKone et al, ). However, some authors suggest that this adjustment reduces ecological validity by failing to replicate the immense variability that typically occurs between different images of the same face in everyday life (Young & Burton, , ). In fact, the matching of two unfamiliar faces of the same identity is a notoriously difficult task (e.g., Jenkins, White, Van Montford, & Burton, ; Young & Burton, , ), even when external features are present and the two images have been collected on the same day (e.g., Bruce et al, ).…”

Section: Introductionmentioning

confidence: 99%

The consistency of superior face recognition skills in police officers

Bate

Frowd

Bennetts

et al. 2019

Applied Cognitive Psychology

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In recent years, there has been increasing interest in people with superior face recognition skills. Yet identification of these individuals has mostly relied on criterion performance on a single attempt at a single measure of face memory. The current investigation aimed to examine the consistency of superior face recognition skills in 30 police officers, both across tests that tap into the same process and between tests that tap into different components of face processing. Overall indices of performance across related measures were found to identify different superior performers to isolated test scores. Further, different top performers emerged for target-present versus target-absent indices, suggesting that signal detection measures are the most useful indicators of performance. Finally, a dissociation was observed between superior memory and matching performance. Super-recognizer screening programmes hould therefore include overall indices summarizing multiple attempts at related tests, allowing for individuals to rank highly on different (and sometimes very specific) tasks.

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Section: Introductionmentioning

confidence: 99%

The consistency of superior face recognition skills in police officers

Bate

Frowd

Bennetts

et al. 2019

Applied Cognitive Psychology

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show abstract

“…The task to recognize similar faces is a computational challenge. It is evident that humans have very strong face recognition abilities and these abilities are superior to known faces but ability to recognize the unfamiliar faces are error-prone [139]. This distinction of face recognition in human lead towards the finding that face recognition depends on different set of facial features for familiar and unfamiliar faces.…”

Section: Face Recognitionmentioning

confidence: 99%

An analytical study of information extraction from unstructured and multidimensional big data

2019

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IntroductionInformation extraction (IE) process extracts useful structured information from the unstructured data in the form of entities, relations, objects, events and many other types. The extracted information from unstructured data is used to prepare data for analysis. Therefore, the efficient and accurate transformation of unstructured data in the IE process improves the data analysis. Numerous techniques have been introduced for different data types i.e. text, image, audio, and video.The advancement in technology promoted the rapid growth of data volume in recent years. The volume, variety (structured, unstructured, and semi-structured data) and velocity of big data have also changed the paradigm of computational capabilities of the systems. IBM estimated that more than 2.5 quintillion bytes of data are generated every Abstract Process of information extraction (IE) is used to extract useful information from unstructured or semi-structured data. Big data arise new challenges for IE techniques with the rapid growth of multifaceted also called as multidimensional unstructured data. Traditional IE systems are inefficient to deal with this huge deluge of unstructured big data. The volume and variety of big data demand to improve the computational capabilities of these IE systems. It is necessary to understand the competency and limitations of the existing IE techniques related to data pre-processing, data extraction and transformation, and representations for huge volumes of multidimensional unstructured data. Numerous studies have been conducted on IE, addressing the challenges and issues for different data types such as text, image, audio and video. Very limited consolidated research work have been conducted to investigate the task-dependent and task-independent limitations of IE covering all data types in a single study. This research work address this limitation and present a systematic literature review of state-of-the-art techniques for a variety of big data, consolidating all data types. Recent challenges of IE are also identified and summarized. Potential solutions are proposed giving future research directions in big data IE. The research is significant in terms of recent trends and challenges related to big data analytics. The outcome of the research and recommendations will help to improve the big data analytics by making it more productive. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Adnan and Akbar J Big Data (2019) 6:91 Malaysia Adnan and Akbar J Big Data (2019) 6:91 RESEARCHday. Among these statistics, it was also predicted that unstructured data from diverse sources will grow up to 90% in few years. IDC estimated that unstructured data will be 95% of the global data in 2020 with estimated 65% annual growth rate [1]. The common characteristics of unstructured data are, (i) it comes in multiple formats...

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“…People can detect with high accuracy minute alterations to the facial configurations of famous (Ge, Luo, Nishimura, & Lee, 2003) or personally familiar faces (Brédart & Devue, 2006;Devue et al, 2007). But on the other hand, we can fail spectacularly to recognise faces of people we have just recently met, or even to match two views of the same unfamiliar individual Young & Burton, 2017; for reviews on the differences between familiar and unfamiliar face processing see Freiwald, Yovel, & Duchaine, 2016;Johnston & Edmonds, 2009;Natu & O'Toole, 2011).…”

Section: New Insights On Real-world Human Face Recognitionmentioning

confidence: 99%

“…However, they do not capture the rich context in which faces actually become familiar (Burke, Taubert, & Higman, 2007;Burton, 2013;Young & Burton, 2017), and they do not typically track changes in familiarity over the extended time course that may be required to produce robust representations. Furthermore, neither approach seriously taxes the abilities of those with superior recognition skills who tend to perform at ceiling on these tests, leaving their limits and the claim that they do not forget faces (see Russell et al, 2009) untested.…”

Section: New Insights On Real-world Human Face Recognitionmentioning

confidence: 99%

New insights on real-world human face recognition

Devue¹,

Wride²,

Grimshaw³

2017

Preprint

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Humans are supposedly expert in face recognition. Because of limitations in existing research paradigms, little is known about how faces become familiar in the real world, or the mechanisms that distinguish good from poor recognisers. Here, we capitalised on several unique features of the television series Game of Thrones to develop a highly challenging test of face recognition that is ecologically grounded and yet controls for important factors that affect familiarity. We show that familiarisation with faces and reliable person identification require much more exposure than previously suggested. Recognition is impaired by the mere passage of time and simple changes in appearance, even for faces we have seen frequently. Good recognisers are distinguished not by the number of faces they recognise, but by their ability to reject novel faces as unfamiliar. Importantly, individuals with superior recognition abilities also forget faces and are not immune to identification errors.

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Recognizing Faces

Cited by 127 publications

References 33 publications

The consistency of superior face recognition skills in police officers

The consistency of superior face recognition skills in police officers

An analytical study of information extraction from unstructured and multidimensional big data

New insights on real-world human face recognition

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