Information and viewpoint dependence in face recognition

Hill, Harold; Schyns, Philippe G.; Akamatsu, Shigeru

doi:10.1016/s0010-0277(96)00785-8

Cited by 228 publications

(234 citation statements)

References 46 publications

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“…However, evidence from a range of sources suggests that effects of view dependence need not necessarily arise from a generalisable ("rotatable") representation. Instead, a small number of canonical views (for example full face, three quarter and profile) can be used to generalize to other intermediate views without significant decrement in recognition performance (see e.g., Hill, Schyns & Akamatsu, 1997;Perrett et al, 1985Perrett et al, , 1998Logothetis, Pauls & Poggio, 1995). This position is consistent with the averaging proposal outlined here.…”

Section: Discussionsupporting

confidence: 71%

Robust representations for face recognition: The power of averages

Burton

Jenkins

Hancock

et al. 2005

Cognitive Psychology

270

297

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We are able to recognise familiar faces easily across large variations in image quality, though our ability to match unfamiliar faces is strikingly poor. Here we ask how the representation of a face changes as we become familiar with it. We use a simple imageaveraging technique to derive abstract representations of known faces. Using Principal Components Analysis, we show that computational systems based on these averages consistently outperform systems based on collections of instances. Furthermore, the quality of the average improves as more images are used to derive it. These simulations are carried out with famous faces, over which we had no control of superficial image characteristics. We then present data from three experiments demonstrating that image averaging can also improve recognition by human observers. Finally, we describe how PCA on image averages appears to preserve identity-specific face information, while eliminating non-diagnostic pictorial information. We therefore suggest that this is a good candidate for a robust face representation.2 Robust representations for face recognition: the power of averages

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Section: Discussionsupporting

confidence: 71%

Robust representations for face recognition: The power of averages

Burton

Jenkins

Hancock

et al. 2005

Cognitive Psychology

270

297

View full text Add to dashboard Cite

show abstract

“…Indeed, identity matching required the recognition of a face under different emotional expressions and under different viewpoints, and this ability is usually considered to involve configural processing (e.g., Hill, Schyns, & Akamatsu, 1997). It is possible, however, that control and autistic subjects used different strategies despite a similar level of performance.…”

Section: Discussionmentioning

confidence: 99%

Spatial Frequency and Face Processing in Children with Autism and Asperger Syndrome

Deruelle

Rondan

Gepner

et al. 2004

J Autism Dev Disord

270

200

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“…Orientation-dependent recognition performance is not limited to individual objects, like faces (e.g., Hill, Schyns, & Akamatsu, 1997) or to objects at the subordinate level of categorization (e.g., Tarr, 1995) but has also been demonstrated for basic level recognition (Hayward & Williams, 2000;Jolicoeur, Corballis, & Lawson, 1998;Lawson & Humphreys, 1998;Murray, 1998;Palmer et al, 1981). Orientation dependency has been observed in the perception of biological motion (Daems & Verfaillie, 1999;Verfaillie, 1993; for a review, see I.…”

Section: Orientation Dependencymentioning

confidence: 99%

Coordinate transformations in object recognition.

Graf¹

2006

Psychological Bulletin

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A basic problem of visual perception is how human beings recognize objects after spatial transformations. Three central classes of findings have to be accounted for: (a) Recognition performance varies systematically with orientation, size, and position; (b) recognition latencies are sequentially additive, suggesting analogue transformation processes; and (c) orientation and size congruency effects indicate that recognition involves the adjustment of a reference frame. All 3 classes of findings can be explained by a transformational framework of recognition: Recognition is achieved by an analogue transformation of a perceptual coordinate system that aligns memory and input representations. Coordinate transformations can be implemented neurocomputationally by gain (amplitude) modulation and may be regarded as a general processing principle of the visual cortex.Keywords: alignment, coordinate transformations, gain modulation, object recognition, reference frames How can we recognize objects regardless of spatial transformations such as plane and depth rotation, size scaling, and position changes? This ability is often discussed under the label object constancy or shape constancy. Even young children recognize objects so immediately and effortlessly that it seems to be a rather ordinary and simple task. However, changes in the spatial relation between observer and object lead to large changes of the image that is projected onto the retina. Hence, to recognize objects regardless of orientation, size, and position is not a trivial problem. No computational system proposed so far can successfully recognize objects over wide ranges of object categories and contexts.Several different approaches have been proposed over the years (for reviews, see Palmeri & Gauthier, 2004;Ullman, 1996). A number of models rely on abstract object representations, which predict that recognition performance is typically invariant regarding spatial transformations (e.g., structural description models; see Hummel & Biederman, 1992;Marr & Nishihara, 1978). In contrast, image-based or view-based models propose that object representations are close to the format of the perceptual input and therefore depend systematically on image transformations (e.g.,

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Information and viewpoint dependence in face recognition

Cited by 228 publications

References 46 publications

Robust representations for face recognition: The power of averages

Robust representations for face recognition: The power of averages

Spatial Frequency and Face Processing in Children with Autism and Asperger Syndrome

Coordinate transformations in object recognition.

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