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INVITED

PAPER

Face Recognition by Humans:

Nineteen Results All Computer

Vision Researchers Should

Know About

Increased knowledge about the ways people recognize each other may help to

guide efforts to develop practical automatic face-recognition systems.

By Pawan Sinha, Benjamin Balas, Yuri Ostrovsky, and Richard Russell

ABSTRACT

A key goal of computer vision re searchers 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 computat ional systems. Eac h re sult is

described briefly and appropriate pointers are provided to

permit an in-depth study of any particular result.

KEYWORDS

Benchmarks; configuration; face pigmentation;

face recognition; human vision; neural correlates; resolution;

visual development

I. INTRODUCTION

Notwithstanding the extensive research effort that has

gone into computational face recognition algorithms, we

have yet to see a system that can be deployed effectively in

an unconstrained setting, with all of the attendant

variability in imaging parameters such as sensor noise,

viewing distance, and illumination. The only system that

does seem to work well in the face of these challenges is

the human visual system. It makes eminent sense,

therefore, to attempt to understand the strategies this bio-

logical system employs, as a first step towards eventually

translating them into machine-based algorithms. With this

objective in mind, we review here 19 important results

regarding face recognition by humans. While these

observations do not constitute a coherent theory of face

recognition in human vision (we simply do not have all the

pieces yet to construct such a theory), they do provide

useful hints and constraints for one. We believe that for

this reason, they are likely to be useful to computer vision

researchers in guiding their ongoing efforts. Of course, the

success of machine vision systems is not dependent on a

slavish imitation of their biological counterparts. Insights

into the functioning of the latter serve primarily as

potentially fruitful starting points for computational

investigations.

We have endeavored to bring together in one place

several diverse results to be able to provide the reader a

fairly comprehensive picture of our current understanding

regarding how humans recognize faces. Each of the results

is briefly described and, whenever possible, accompanied

by its implications for computer vision. While the

descriptions here are not extensive for reasons of space,

we have provided relevant pointers to the literature for a

more in-depth study. The results are organized along the

following broad themes.

Recognition as a function of available spatial resolution

Result 1: Humans can recognize familiar faces in

very low-resolution images.

Result 2: The ability to tolerate degradations in-

creases with familiarity.

Manuscript received July 12, 2005; revised March 15, 2006.

P. Sinha, B. Balas, and Y. Ostrovsky are with the Department of Brain and Cognitive

Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139 USA

(e-mail: psinha@mit.edu; bjbalas@mit.edu; yostr@mit.edu).

R. Russell is with the Department of Psychology, Harvard University, Cambridge,

MA 02138 USA (e-mail: rrussell@fas.harvard.edu).

Digital Object Identifier: 10.1109/JPROC.2006.884093

1948 Proceedings of the IEEE | Vol. 94, No. 11, November 2006 0018-9219/$20.00

2006 IEEE

Result 3: High-frequency information by itself is

insufficient for good face recognition

performance.

The nature of processing: Piecemeal versus holistic

Result 4: Facial features are processed holistically.

Result 5: Of the different facial features, eyebrows

are among the most important for

recognition.

Result 6: The important configural relationships

appear to be independent across the width

and height dimensions.

The nature of cues used: Pigmentation, shape and motion

Result 7: Face-shape appears to be encoded in a

slightly caricatured manner.

Result 8: Prolonged face viewing can lead to high-

level aftereffects, which suggest proto-

type-based encoding.

Result 9: Pigmentation cues are at least as impor-

tant as shape cues.

Result 10: Color cues play a significant role, espe-

cially when shape cues are degraded.

Result 11: Contrast polarity inversion dramatically

impairs recognition performance, possi-

bly due to compromised ability to use

pigmentation cues.

Result 12: Illumination changes influence general-

ization.

Result 13: View-generalization appears to be medi-

ated by temporal association.

Result 14: Motion of faces appears to facilitate

subsequent recognition.

Developmental progression

Result 15: The visual system starts with a rudimen-

tary preference for face-like patterns.

Result 16: The visual system progresses from a piece-

meal to a holistic strategy over the first

several years of life.

Neural underpinnings

Result 17: The human visual system appears to de-

vote specialized neural resources for face

perception.

Result 18: Latency of responses to faces in infero-

temporal (IT) cortex is about 120 ms, sug-

gesting a largely feedforward computation.

Result 19: Facial identity and expression might be

processed by separate systems.

A. Recognition as a Function of Available

Spatial Resolution

1) Result 1: Humans Can Recognize Familiar Faces in Very

Low-Resolution Images: Progressive improvements in cam-

era resolutions provide ever-greater temptation to use

increasing amounts of detail in face representations in

machine vision systems. Higher image resolutions allow

recognition systems to discriminate between individuals

on the basis of fine differences in their facial features. The

advent of iris-based biometric systems is a case in point.

However, the problem that such details-based schemes

often have to contend with is that high-resolution images

are not always available. This is particularly true in

situations where individuals have to be recognized at a

distance. In order to design systems more robust against

image degradations, we can turn to the human visual

system for inspiration. Everyday, we are confronted with

the task of face identification at a distance and must extract

the critical information from the resulting low-resolution

images. Precisely how does face identification perfor-

mance change as a function of image resolution?

Pioneering work on face recognition with low-resolution

imagery was done by Harmon and Julesz [30], [31].

Working with block averaged images of familiar faces, they

found high recognition accuracies even with images

containing just 16  16 blocks. Yip and Sinha [89] found

that subjects could recognize more than half of an un-

primed set of familiar faces that had been blurred to have

equivalent image resolutions of merely 7  10 pixels (see

Fig. 1), and recognition performance reached ceiling level

at a resolution of 19  27 pixels. While the remarkable

tolerance of the human visual system to resolution

reduction is now indisputable, we do not have a clear

idea of exactly how this is accomplished. At the very least,

this result demonstrates that fine featural details are not

necessary to obtain good face recognition performance.

Furthermore, given the indistinctness of the individual

features at low resolutions, it appears likely that diagnos-

ticity resides in their overall configuration. However, pre-

cisely which aspects of this configuration are important,

and how we can computationally encode them, are open

questions.

2) Result 2: The Ability to Tolerate Degradations Increases

With Familiarity: In trying to uncover the mechanisms

underlying the human ability to recognize highly degraded

face images, we might wonder whether this is the result of

some general purpose compensatory processes, i.e., a

biological instantiation of model-free Bsuper resolution.[

However, the story appears to be more complicated. The

ability to handle degradations increases dramatically with

amount of familiarity. Bruce et al. [9] demonstrated ob-

servers’ poor performance on the task of matching two

different photographs of an unfamiliar person. Burton et al.

[10] have shown that observers’ recognition performance

with low-quality surveillance video is much better when the

individuals pictured are familiar colleagues, rather than

those with whom the observers have interacted infrequent-

ly. Additionally, body structure and gait information are

much less useful for identification than facial information,

Sinha et al.: Face Recognition by Humans: Nineteen Results Researchers Should Know About

Vol. 94, No. 11, November 2006 | Proceedings of the IEEE 1949

even though the effective resolution in that region is very

limited. Recognition performance changes only slightly

after obscuring the gait or body, but is affected dramatically

when the face is hidden, as illustrated in Fig. 2. This does

not appear to be a skill that can be acquired through general

experience; even police officers with extensive forensic

experience perform poorly unless they are familiar with the

target individuals. The fundamental question this finding,

and others like it [49], [66], bring up is the following: How

does the facial representation and matching strategy used

by the visual system change with increasing familiarity, so

as to yield greater tolerance to degradations? We do not yet

know exactly what aspect of the increased experience with

a given individual leads to an increase in the robustness of

the encoding; is it the greater number of views seen or is

the robustness an epiphenomenon related to some bio-

logical limitations such as slow memory consolidation

rates? Notwithstanding our limited understanding, some

implications for computer vision are already evident. In

considering which aspects of human performance to take

as benchmarks, we ought to draw a distinction between

familiar and unfamiliar face recognition. The latter may

end up being a much more modest goal than the former

and might constitute a false goal towards which to strive.

The appropriate benchmark for evaluating machine-based

face recognition systems is human performance with

familiar faces.

3) Result 3: High-Frequency Information by Itself Does Not

Lead to Good Face Recognition Performance: We have long

been enamored of edge maps as a powerful initial repre-

sentation for visual inputs. The belief is that edges capture

the most important aspects of images (the discontinuities)

while being largely invariant to shallow shading gradients

that are often the result of illumination variations. In the

context of human vision as well, line drawings appear to be

sufficient for recognition purposes. Caricatures and quick

pen portraits are often highly recognizable. Do these

observations mean that high spatial frequencies are

critical, or at least sufficient, for face recognition? Several

researchers have examined the contribution of different

spatial frequency bands to face recognition [14], [21].

Their findings suggest that high spatial frequencies might

not be too important for face perception. In the particular

domain of line drawings, Graham Davies and his col-

leagues have reported [16] that images which contain

exclusively contour information are very difficult to re-

cognize (specifically, they found that subjects could recog-

nize only 47% of the line drawings compared to 90% of the

original photographs; see Fig. 3). How can we reconcile

such findings with the observed recognizability of line

drawings in everyday experience? Bruce and colleagues

[6], [7] have convincingly argued that such depictions do,

in fact, contain significant photometric cues and that the

contours included in such a depiction by an accomplished

artist correspond not just to a low-level edge map, but in

Fig. 2. Frames from video-sequences used in Burton et al. [10] study.

(a) Original input. (b) Body obscured. (c) Face obscured. Based on

results from such manipulations, researchers concluded that

recognition of familiar individuals in low-resolution video is based

largely on facial information.

Fig. 1. Unlike current machine-based systems, human observers are able to handle significant degradations in face images. For instance,

subjects are able to recognize more than half of all familiar faces shown to them at the resolution depicted here. Individuals shown in

order are: Michael Jordan, Woody Allen, Goldie Hawn, Bill Clinton, Tom Hanks, Saddam Hussein, Elvis Presley, Jay Leno,

Dustin Hoffman, Prince Charles, Cher, and Richard Nixon.

Sinha et al.: Face Recognition by Humans: Nineteen Results Researchers Should Know About

1950 Proceedings of the IEEE | Vol. 94, No. 11, November 2006

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xfj8_2

2016-06-30

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