Why are moving faces easier to recognize?

Lander, Karen; Chuang, Lewis L.

doi:10.1080/13506280444000382

Cited by 93 publications

(83 citation statements)

References 26 publications

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“…The recognition advantage found in these studies is reduced when the precise dynamic characteristic of the observed pattern of motion is disrupted by presenting the frames in a jumbled order or in slow or speeded motion (Lander and Bruce 2000). In addition, the recognition advantage appears only to be present for degraded familiar faces that are rated as having`distinctive', rather than typical, facial movement patterns (Lander and Chuang 2005). Distinctive facial movements are more likely to be idiosyncratic than typical facial movements.…”

Section: (Figure 1)mentioning

confidence: 84%

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Repetition Priming and Recognition of Dynamic and Static Chimeras

Steede

Hole

2006

Perception

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Article (Published Version) http://sro.sussex.ac.uk Steede, Leslie L and Hole, Graham J (2006) Repetition priming and recognition of dynamic and static chimeras. Perception, 35 (10). pp. 1367 -1382 This version is available from Sussex Research Online: http://sro.sussex.ac.uk/14916/ This document is made available in accordance with publisher policies and may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the URL above for details on accessing the published version. Copyright and reuse:Sussex Research Online is a digital repository of the research output of the University.Copyright and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable, the material made available in SRO has been checked for eligibility before being made available.Copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. This model proposes a number of hypothetical units that are involved in face processing. Face-recognition units (FRUs) are believed to store visual descriptions of known faces. Personal-identity nodes (PINs) allow access to semantic information about familiar faces. Semantic-information units (SIUs) store semantic information about familiar faces (eg whether the person is a film star or a politician). Excitatory links connect units that are associated with the same or related faces, and inhibitory links connect units associated with different or unrelated faces.When the face-recognition system is presented with a familiar face, a FRU corresponding to that face becomes activated. If the level of activation passes a threshold, activation flows from the FRU to the PIN, causing the PIN associated with the familiar face to become active. When the PIN reaches a threshold, the face is recognised as familiar. PIN activation then flows to the SIU link, which allows access to semantic information about familiar faces.The IAC model has been successful in describing various phenomena found in the face-recognition literature (Burton et al 1990). Of particular relevance to this study is that the functional mechanisms are capable of explaining repetition or identity priming. Repetition priming is the facilitation found in recognising a familiar face as a result of it being encountered previously (Bruce and Valentine 1985). Repetition priming is believed to occur because the initial presentation of a familiar face leads to activation of the link between the FRU and PIN for that individual. If the same face is presented later, activation flows more quickly via th...

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Section: (Figure 1)mentioning

confidence: 84%

“…Distinctive facial movements are more likely to be idiosyncratic than typical facial movements. Taken together, these findings have led researchers to believe that the recognition advantage for recognising degraded dynamic familiar faces appears to be due to the presence of a dynamic facial signature Lander and Chuang 2005).…”

Section: (Figure 1)mentioning

confidence: 99%

Repetition Priming and Recognition of Dynamic and Static Chimeras

Steede

Hole

2006

Perception

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“…Others argue that this does not necessarily reflect a true dynamic effect though, and recognition might actually improve because the number of static frames contained within a moving sequence increases (Lander & Chuang, 2005). Lander, Christie and Bruce (1999) have however shown that when the same frames were displayed either as a static array or animated sequence, identity recognition was still significantly higher for the moving sequence.…”

Section: Introductionmentioning

confidence: 99%

Categorizing identity from facial motion

Girges

Spencer

O’Brien

2015

Quarterly Journal of Experimental Psychology

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Advances in marker--less motion capture technology now allow the accurate replication of facial motion and deformation in computer--generated imagery (CGI). A forced--choice discrimination paradigm using such CGI facial animations showed that human observers can categorise identity solely from facial motion cues. Animations were generated from motion captures acquired during natural speech, thus eliciting both rigid (head rotations and translations) and nonrigid (expressional changes) motion. To limit interferences from individual differences in facial form, all animations shared the same appearance. Observers were required to discriminate between different videos of facial motion and between the facial motions of different people. Performance was compared to the control condition of orientation--inverted facial motion. The results show that observers are able to make accurate discriminations of identity in the absence of all cues except facial motion. A clear inversion effect in both tasks provided consistency with previous studies, supporting the configural view of human face perception. The accuracy of this motion capture technology thus allowed stimuli to be generated which closely resembled real moving faces. Future studies may wish to implement such methodology when studying human face perception.

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“…14). Experiments with famous (i.e., highly familiar) faces [42] again showed a facilitation in recognition with dynamic cues from expressive or talking movements, but not from rigid motion. Facilitation was most pronounced for faces whose movement was judged as Bdistinctive.…”

Section: ) Results 14: Motion Of Faces Appears To Facilitatementioning

confidence: 90%

Attendance Control System Based on Face Recognition Algorithms

Sinetsky¹

2016

Univ. News. N.-C. Reg. Techn. Sci. Ser.

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| A key goal of computer vision researchers is to create automated face recognition systems that can equal, and eventually surpass, human performance. To this end, it is imperative that computational researchers know of the key findings from experimental studies of face recognition by humans. These findings provide insights into the nature of cues that the human visual system relies upon for achieving its impressive performance and serve as the building blocks for efforts to artificially emulate these abilities. In this paper, we present what we believe are 19 basic results, with implications for the design of computational systems. Each result is described briefly and appropriate pointers are provided to permit an in-depth study of any particular result.

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Why are moving faces easier to recognize?

Cited by 93 publications

References 26 publications

Repetition Priming and Recognition of Dynamic and Static Chimeras

Repetition Priming and Recognition of Dynamic and Static Chimeras

Categorizing identity from facial motion

Attendance Control System Based on Face Recognition Algorithms

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