The role of movement in the recognition of famous faces

Lander, Karen; Christie, Fiona; Bruce, Vicki

doi:10.3758/bf03201228

Cited by 188 publications

(234 citation statements)

References 21 publications

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“…Indeed, a less pronounced inversion effect has been observed when faces are shown dynamically (Knappmeyer, Thornton & Bülthoff, 2003;Lander, Christie & Bruce, 1999;Hill & Johnston, 2001). More recent investigations using composite faces also support the featural influence hypothesis of facial motion perception.…”

Section: Running Head: Identity From Facial Motion 14mentioning

confidence: 99%

“…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%

“…They found accuracy to improve only when faces were displayed dynamically relative to a single static image. Later studies implementing other types of impoverished stimuli (threshold processed videos, blurred/pixelated clips or limited frame sequences) also report a beneficial effect of motion during familiar face recognition (Lander, Bruce & Hill, 2001;Lander & Bruce, 2000;Lander, Christie & Bruce, 1999).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Running Head: Identity From Facial Motion 14mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Categorizing identity from facial motion

Girges

Spencer

O’Brien

2015

Quarterly Journal of Experimental Psychology

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“…Christie & Bruce 56 and Bruce et al 39 report no benefits, while Pike et al 57 find advantages for study of moving items in recognition memory), but there have recently been some intriguing demonstrations that motion can aid the identification of familiar faces seen in images which are otherwise difficult to recognise. 58,59 . This suggests that as faces become familiar, their characteristic patterns of movement may become represented in memory as well as the static form.…”

Section: Current Research: Adding Dimensionsmentioning

confidence: 99%

Recognition of unfamiliar faces

Hancock¹,

Bruce²,

Burton³

2000

Trends in Cognitive Sciences

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506

399

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SummaryPeople are excellent at identifying faces familiar to them, even from very low quality images, but are bad at recognising, or even matching, faces that are unfamiliar. In this review we shall consider some of the factors which affect our abilities to match unfamiliar faces. Major differences in orientation (e.g. inversion) or greyscale information (e.g. negation) affect face processing dramatically, and such effects are informative about the nature of the representations derived from unfamiliar faces, suggesting that these are based on relatively low-level image descriptions. Consistent with this, even relatively minor differences in lighting and viewpoint create problems for human face matching, leading to potentially important problems over the use of images from security video images. The relationships between different parts of the face (its "configuration") are as important to the impression created of an upright face as local features themselves, suggesting further constraints on the representations derived from faces. The review then turns to consider what computer face recognition systems may contribute to understanding both the theory and the practical problems of face identification. Computer systems can be used as an aid to person identification, but also in an attempt to model human perceptual processes. There are many approaches to computer recognition of faces, including ones based on low-level image analysis of whole face images, which have potential as models of human performance. Some systems show significant correlations with human perceptions of the same faces, for example recognising distinctive faces more easily. In some circumstances, some systems may exceed human abilities on unfamiliar faces. Finally, we look to the future of work in this area, that will incorporate motion and three-dimensional shape information.

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“…For example, participants can recognize the faces of well-known politicians and celebrities more accurately from videotaped images than from static images. This finding is especially salient when the faces are presented in sub-optimal viewing formats (e.g., blurred, inverted, or pixilated displays; [60,64,66,67]). Thus it seems that motion becomes more important as a cue to identity when the viewing conditions are sub-optimal.…”

Section: Recognizing Moving Facesmentioning

confidence: 99%

Predicting Human Performance for Face Recognition

2006

Face Processing

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The ability of humans to recognize faces provides an implicit benchmark for gauging the performance of automatic face recognition algorithms. IntroductionHuman face recognition abilities are impressive by comparison to the performance of many current automatic face recognition systems. Such is the belief of many psychologists and computer scientists. This belief, however, is supported more by anecdotal impression than by scientific evidence. In fact, there are relatively few systematic and direct comparisons between the performance of humans and automatic face recognition algorithms, (though see [84], for exceptions). Comparisons of these sorts can be carried out both at a quantitative and qualitative level. On the qualitative side, as the field of automatic face recognition develops, the assessment of working systems has expanded to consider, not only overall levels of performance, but performance under more restricted and targeted conditions. It may thus be important to know, for example, whether an algorithm performs accurately recognizing "male faces of a certain age", "female faces of a certain ethnicity", or faces that are "typical" in the context

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The role of movement in the recognition of famous faces

Cited by 188 publications

References 21 publications

Categorizing identity from facial motion

Categorizing identity from facial motion

Recognition of unfamiliar faces

Predicting Human Performance for Face Recognition

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