1 

Quantifying How Lighting and Focus 

Affect Face Recognition Performance

Phillips, P. J.

Beveridge, J. R.

Draper, B.

Bolme, D.

Givens, G. H.

Lui, Y. M.

NISTIR 7674
 2 

NISTIR 7674

Quantifying How Lighting and Focus 

Affect Face Recognition Performance

Phillips, P. J.

NIST 

Information Access Division 

Beveridge, J. R.

Draper, B.

Bolme, D.

Givens, G. H.

Lui, Y. M.

Colorado State University 

February 2010

U.S. Department of Commerce 

Gary Locke, Secretary 

National Institute of Standards and Technology 

Patrick D. Gallagher, Director
Quantifying How Lighting and Focus Affect Face Recognition Performance∗

J. Ross Beveridge David S. Bolme Bruce A. Draper Geof H. Givens Yui Man Lui

Colorado State University

Fort Collins, CO 80521, USA

beveridge@cs.colostate.edu

P. Jonathon Phillips

National Institute of Standards and Technology, Gaithersburg, MD 20899

Abstract

Recent studies show that face recognition in uncontrolled images remains a challenging problem, although the

reasons why are less clear. Changes in illumination are one

possible explanation, although algorithms developed since

the advent of the PIE and Yale B data bases supposedly

compensate for illumination variation. Edge density has

also been shown to be a strong predictor of algorithm failure on the FRVT 2006 uncontrolled images: recognition is

harder on images with higher edge density.

This paper presents a new study that explains the edge

density effect in terms of illumination and shows that top

performing algorithms in FRVT 2006 are still sensitive to

lighting. This new study also shows that focus, originally

suggested as an explanation for the edge density effect, is

not a significant factor. The new lighting model developed

in this study can be used as a measure of face image quality.

1. Introduction

Face recognition technology has made significant

progress since the early 1990’s. Current face recognition

systems are highly accurate for face images collected in studios with consistent pose, focus and lighting. The Face

Recognition Vendor Test (FRVT) 2006 showed that it is

possible to achieve a false reject rate (FRR) of 0.008 at

a false accept rate (FAR) of 0.001 for well controlled images [16]. For many in the research community, this is now

considered to be a “solved problem”.

∗The work was funded in part by the Technical Support Working Group

(TSWG) under Task T-1840C. PJP was supported by the Department of

Homeland Security, Director of National Intelligence, Federal Bureau of

Investigation and National Institute of Justice. The identification of any

commercial product or trade name does not imply endorsement or recommendation by Colorado State University or the National Institute of Standards and Technology.

The FRVT 2006 also included a set of uncontrolled images taken in hallways or outside with variations in lighting, focus, near fontal pose, and expression, among other

factors. For these images, the best reported performance

was a FRR of 0.12 at an FAR of 0.001; among the better

algorithms, the FRR ranged from 0.12 to 0.38 at an FAR of

0.001 [16, 3]. Two other useful uncontrolled data sets are

Labeled Faces in the Wild [8] and the PubFig [11]. Both

include images collected from the World Wide Web without explicitly controlling for factors such as lighting, pose,

focus or expression. Error rates for recent algorithms on Labeled Faces in the Wild range around 0.20 [17] to 0.15 [11].

For the PubFig data set the best total error rate is 0.22 [11].

The face recognition research community has considered a number of factors to explain what makes recognition

harder on uncontrolled data. The most common explanations involve illumination, pose and expression [14, 19, 12].

Other factors studied include age of the person, gender, expression, image resolution, and time between images [13].

Of these, changes in illumination ranks high on most

researcher’s list of factors making face recognition hard.

To study the effects of changes in pose and illumination,

the Carnegie Mellon University Pose, Illumination, and Expression (PIE) and Yale B data sets [19, 12] were created.

In these data sets, changes in pose and illumination are systematically varied. Since the release of these data sets, there

has been a dramatic decrease in the error rate associated

with change in illumination direction. However, up to now

it has not been possible to assess quantitatively the impact

of changing illumination on data sets such as FRVT 2006

that do not explicitly control for illumination direction.

Other directly measurable quality measures of face images have been proposed that predict when algorithms will

fail. In particular, Beveridge et al. [3] show that higher edge

density is strongly associated with recognition failure on the

FRVT 2006 uncontrolled images. What they did not provide was a physical explanation. Is edge density reflecting

1
changes in focus? Is it responding to changes in illumination? Perhaps neither; edge density could be responding to

some other unidentified aspect of the images.

To answer these questions, this paper presents a model

for estimating illumination direction based on prior work

by Sim and Kanade [20]. It also introduces a new measure

of image focus. Finally, it presents a series of of four experiments on the same FRVT 2006 data studied in [3] that

unravel the relative importance of lighting and focus, and

relate both back to the original finding that higher edge density predicts recognition failure.

The most significant finding is that lighting accounts for

the edge density effect. In other words, the best explanation for why edge density was previously found to be such

a strong predictor of recognition performance is that it was

indirectly capturing information about lighting. The second

most significant finding is that focus, when quantified using

a better measure, plays little or no role in making recognition easier or harder. This study represents the first time, to

our knowledge, that an illumination measure has been directly incorporated into a multi-factor study of face recognition performance on a large uncontrolled data set.

2. Motivation

Beveridge et al noted that low edge densities predict successful recognition in FRVT2006. Looking more closely at

the data, we noticed that high edge density was often associated with outdoor images taken under harsh lighting, while

low edge densities were associated with more uniform lighting. Furthermore, it appeared that recognition often failed

when the face was illuminated from the side by direct sunlight. This makes sense to anyone familiar with the algorithms, since such lighting introduces both strong shadows

and strong asymmetry.

The apparent association between strong side lighting

and recognition failure raises the question of whether lighting conditions might explain recognition performance as

well or better than edge density, while supplying a physical explanation. Telling the face recognition community

that algorithms fail for images with high edge density raises

the question of how one operationally ’controls’ edge density. An explanation in terms of how the face is illuminated

is qualitatively different and operationally more useful.

A separate lingering question concerns image focus. Reviewing the images by hand reinforced our belief that edge

density was responding to many aspects of the scene and

images. Poorly focused images did have low edge density,

but edge density clearly responds to other factors, too. From

an operational standpoint, knowing if poor focus contributes

to recognition failure is important, so the need for an effective quantitative measure of image focus persists.

In response to the questions raised by our visual inspection of the data, we have developed a lighting model and a

focus measure. These measures are described in Section 4.

Armed with these quantitative means of assessing lighting

and focus, we investigated four conjectures:

Conjecture 1 Lighting subsumes setting (i.e., indoors versus outdoors) as a predictor of performance.

Conjecture 2 Lighting subsumes edge density as a predictor of performance.

Conjecture 3 Focus explains recognition failure better

than edge density.

Conjecture 4 Since focus is distinct from lighting, predictions about recognition failure using both will be better

than either alone.

The basis for these conjectures is our hypothesis that edge

density and setting are poor quality measures that only indirectly relate to performance. Edge density and setting confound different aspects in which images (and hence recognition performance) may vary. In comparison, lighting and focus isolate two physical components of image quality. Ideally they should be nearly orthogonal, and they may capture nearly all of the signal inherent in the inferior measures

and perhaps additional information as well. Experiments

designed to test each of these conjectures and the interpretation of their results are presented in Section 5.

3. Background

The work presented here draws upon prior work in lighting estimation, focus estimation and performance evaluation. Prior work in each of these areas is reviewed here.

3.1. Lighting Estimation

There are many sources of variations that can confuse

a person’s identity. Illumination is one of those variations

in imagery. In fact, it has been argued that changes in illumination can make two images of the same person less

similar than two images of different people [14]. A considerable literature has been developed that addresses a variety

of techniques for estimating illumination, and in particular,

Sim and Kanade [20] were the first to employ kernel regression for illumination estimation.

The model presented in Section 4.1 also uses kernel regression and is similar in general approach to that of Sim

and Kanade. It should be noted, however, that the primary

goal of Sim and Kanade was to remove lighting artifacts.

Our goal is to estimate the lighting angle, from frontal to

side. Hence, the approaches are related, but not identical.

3.2. Focus Estimation

Krotkov proposed edge density as a measure of focus in

1989 [10]. It was compared to a number of alternatives,
and found superior. In particular, it was found to do a better job than alternative approaches that were based upon the

spectral energy signature of an image. Beveridge et al. [3]

adapted the measure slightly by restricting it to only the region of image containing the face.

3.3. Performance Evaluation

Empirical evaluation and performance evaluation have a

long and rich history in computer vision in general and face

recognition in particular [7, 9, 21]. For example, over the

past decade there have been a series of empirical evaluation

workshops associated with CVPR. In face recognition, there

have been a string of evaluations starting with FERET[15]

and moving through FRVT 2006 [16]. There is also increasing interest in scenarios more closely associated with the

web: see for example the Labeled Faces in the Wild results

page 1.

Two lines of work are particularly relevant to the results

presented here. The first is that of Beveridge et al. [4, 3]

that has proposed the use of a Generalized Linear Mixed

Effect Model (GLMM) to relate a set of factors, i.e., covariates, to the probability that a face recognition algorithm

will succeed. As already discussed above, that work established that a particularly simple image measure, edge density, is strongly related to face recognition performance in

the FRVT 2006. What that work did not do was to provide,

in any substantive manner, a practical explanation of why.

The second line of closely related research includes efforts to predict the success or failure of biometric algorithms, in our case, face recognition algorithms, based on

objective quantitative information available at the time of

recognition. If one associates these measures with biometric quality, the task is equivalent to that of predicting

recognition success based upon biometric quality. One recent good example of such work is that of Grother and

Tabassi [6]. It should also be noted that prediction of recognition success may also be based upon sets of related match

scores rather than on a directly measurable property of the

biometric; an intriguing example of this approach is the recent work by Scheirer and Boult [18].

Our work below will bring together some aspects of

each approach. The starting point in the analysis will be

a GLMM comparable to that developed by Beveridge et al.

From the second line of work, we will use the model’s ability to predict success or failure as a way to assess the value

of an associated factor. The factors examined here are edge

density, focus, lighting and setting.

4. Approach

This section sets forth our new lighting estimation

model, focus measure, and the approach that will be used

1http://vis-www.cs.umass.edu/lfw/results.html

to test when one covariate subsumes another.

4.1. Lighting Estimation

The illumination model is trained on the CMU-PIE [19]

imagery and is constructed to return a single number indicating the extent to which the face is being lit from the side.

Recall that CMU-PIE has images for 68 subjects, and each

subject has 24 illumination variants. There are also images

with the room lights on and the room lights off. Here we

use only the images with room lights on.

To eliminate the identity effect, we average all 68 subjects along each illumination variant. Hence, 24 illumination variant images are used to train the lighting model.

The lighting model is defined as follows. Let μk be the

average image in illumination variant k. We apply kernel

regression [1] to estimate the lighting coefficients described

as follows:

xˆ =

Pn

P

k=1 αkμk

n

k=1 αk

(1)

αk = exp(−

k x − μk k

2

σ

2

)

where x is the estimated image, n is 24 (the number of variants), and σ is 10. As Equation (1) shows, xˆ is the reconstructed lighting images from the training set.

According to the illumination cone principle [2], any image in the illumination cone can be reconstructed by a linear

combination of extreme rays given as:

xˆ = max (Bs, 0) (2)

where B is an illumination basis, s is the lighting coefficients, and 0 is used to remove the negative values corresponding to the shadowed surface. Therefore, we obtain the

lighting coefficients as:

s = B

−1xˆ (3)

where B is the average illumination μ in our case. The dominant illumination direction is determined as:

d

∗ = argmax s (4)

and the lighting estimation γ is computed as:

γ = s(d

∗

)W(d

∗

) (5)

where W is a predefined weighted vector which is [0 0 -3 -3

-2 -2 -1 -1 1 1 1 1 1 1 1 -1 -2 -3 -2 -1 1 1 1 0]. An example of lighting estimation is given in Figure 1. The predefined weights correspond to the lighting directions from the

CMU-PIE dataset where positive indicates frontal illumination, negative denotes side illuminations, and zero indicates

that no flashes were applied.
Figure 1. Example of lighting estimation: left (-0.240), middle (-

0.237), right (0.068)

4.2. A Strong Edge Motion Compensated Focus

Measure

The problem with edge density as a measure of focus

across a range of images is that edge density responds to

many different aspects of the person and scene. These include lighting, glasses and hair across the forehead. To correct for this problem, a new motion blur compensated focus

measure is introduced. The measure also starts by using Sobel operators to compute image gradients. Instead of measuring the average gradient magnitude it measures the gradient of the strongest edges, since the gradient of the strongest

edge is a much better predictor of image focus than the average gradient. It also measures motion blur, and equates

motion blur with poor focus.

An image suffering from motion blur is smooth in the direction of motion and sharp in the orthogonal direction. To

measure the strongest edges in the direction of motion, PCA

is applied to the horizontal and vertical components of the

image gradient vectors. If motion blur is present, the PCA

component corresponding to the smallest principal component indicates the direction of motion. By projecting the

gradient vectors onto the smallest principal component, the

new measure estimates the gradient in the blurriest direction, and therefore measures blur caused by both focus and

motion.

In our earliest version of this new algorithm, the largest

edge in the direction of motion was used to estimate focus.

However, as one might expect, this approach is unstable because the maximum value is often an outlier and not representative of the focus in the image. For this reason we select

the edge strength associated with the 97.5% quantile. This

is close enough to the maximum to measure the gradient of

a strong edge, but also reduces the chance of selecting an

outlier.

To compare the new focus measure to edge density, 54

images were hand selected to span a range from poorly focused to well focused. This set also included images with

blur due to motion. The 54 images were then ranked by alternative measures of focus in order to assess how well these

measures performed. As expected, the ranking imposed by

edge density did not align well with our judgment about

focus. In contrast, the Strong Edge Motion Compensated

(SEMC) focus measure just described ranked images in a

manner consistent with our judgement. Images with motion blur were included in this test, and the steps described

above for handling motion blur resulted in those images being scored as out of focus.

Figure 2 compares some of the highest and lowest edge

density images to some of the highest and lowest SEMC

focus images. The highest edge density images are always

outside with strong lighting on the face and shadows, while

the lowest edge density images are always indoors with neutral lighting. The SEMC focus measure seems to break the

dependency on lighting. Outdoor images with strong shadows are often included with the low group because of poor

focus, and in-focus images with neutral lighting are often

found in the high focus group.

Lowest 5% of Edge Density

Highest 5% of Edge Density

Lowest 5% of SEMC Focus

Highest 5% of SEMC Focus

Figure 2. This figure shows some selected images from the highest

5% and lowest 5% of the edge density and SEMC focus measures.
4.3. Comparing GLMMs to Assess Importance

The conjectures posed in Section 2 mostly involve statements about the relative value of one factor (covariate) versus another. It is possible to approach such questions with

a single statistical model including all significant covariates and their significant interactions. Indeed, the statements about the importance of edge density by Beveridge

et al. [3, 5] does just that. However, one drawback of this

approach is that for the sample sizes relevant here, most if

not all of the proposed covariates and their interactions may

turn out to be significant and hence warrant inclusion in the

GLMM. The combinatorics of model selection quickly surpass one’s ability to make sense of the results.

Another problem with that approach is its intent. With

the GLMM approach, one obtains (for a given set of covariates) estimated probabilities of verification. The relative

performance of models (and hence the comparative value of

predictors in two competing models) is not necessarily directly related to the estimates of verification probabilities or

model parameters. Instead, it is related to the proportion of

the total variability in the data explained by each model.

A natural way to quantify that criterion is to ask how

many cases each model predicts correctly. Note that each

GLMM may be viewed as a mapping from the space of covariates to a predicted probability of successful verification

at a specific false accept rate. Moreover, the prediction is

relative to specific match pairs. For the FRVT 2006 analysis, the match pairs consist of a highly controlled target

image and a less controlled query image taken either in a

hallway or outdoors.

Thus, it is possible to test how well each GLMM predicts the actual outcomes, i.e. success or failure for each

match pair. Specifically, the probability of verification may

be thresholded in order to create a binary classifier: predict

successful verification if the probability of success is above

a threshold τ . In one sense, this does an injustice to the

model in terms of absolute quality, because it throws away

the fine gradations in predicted probabilities of verification.

However, as a means of making relative comparisons between the predictive power of alternative covariates in the

context of a larger statistical model, it works well. Indeed,

as the threshold τ is varied it is possible to create a standard

ROC plot with one curve per GLMM.

Each of the four conjectures in Section 2 will be tested

by assessing the relative predictive power of a carefully chosen set of candidate models. Let us illustrate with the first

Conjecture in Section 2, namely that lighting subsumes setting. To test this conjecture, four distinct GLMMs may be

created. They are:

1 A baseline GLMM without either lighting or setting.

2 Baseline plus an effect for lighting.

3 Baseline plus an effect for setting.

4 Baseline plus effects for lighting and setting.

By comparing the performance of these four models, we

can assess the relative importance of lighting and setting. A

set of four ROC curves compares the models. When interpreting these ROC plots, it is important to remember that

they are measuring the predictive power of alternative models for predicting whether verification succeeds or fails, the

ROC plots are NOT reporting the False Accept Rate and

False Failure Rate of the actual face recognition algorithms.

5. Findings

Beveridge et al. [3] reported results for a GLMM that

used fused similarity scores from three top performing

FRVT 2006 algorithms. Our first step was to replicate that

GLMM and to then remove from it all effects associated

with edge density2and setting. Note the resulting model

still includes other covariates including gender, race, size of

the face and whether the person was wearing glasses in the

uncontrolled image.

The next step was to add back into this baseline GLMM

main effects for covariates in the pattern described above

in order to test each of the four conjectures presented in

Section 2. The ROC plots for each associated experiment

are presented in Figure 3. The findings are as follows.

5.1. Experiment 1: Lighting and Setting

Our conjecture, prior to running this experiment, was

that lighting would subsume setting in the sense that whatever makes recognition more difficult outdoors would be

entirely captured by our newly developed measure of lighting. The ROC presented in Figure 3 supports this conjecture. Note the order, from lowest to highest equal error rate

is: Lighting+Setting, Lighting Only, Setting Only and Baseline. The primary result seen here is that models that use the

lighting variable are superior to those that do not. There is a

small but notable distinction between the Lighting+Setting

model and the Lighting Only Model, indicating that lighting

may not capture 100% of the complex amalgam of factors

that inherently differ between settings. Nevertheless, it is

remarkable how fully the relationship between setting and

performance can be explained solely by our lighting measure.

5.2. Experiment 2: Lighting and Edge Density

Our conjecture was that lighting would subsume edge

density. The ROC presented in Figure 3 fully supports this

conjecture. Note the ROC curves for the Lighting Only

and Lighting+Edge Density GLMMs are nearly identical,

2

In [3] the edge density covariate was often described as FRIFM.
0.10 0.15 0.20 0.25 0.30 0.35 0.40

0.10 0.15 0.20 0.25 0.30 0.35 0.40

Experiment 1: Lighting and Setting

False Success Rate

False Failure Rate

Baseline

Setting Only

Lighting Only

Lighting + Setting

0.10 0.15 0.20 0.25 0.30 0.35 0.40

0.10 0.15 0.20 0.25 0.30 0.35 0.40

Experiment 2: Lighting and Edge Density

False Success Rate

False Failure Rate

Baseline

Edge Density Only

Lighting Only

Lighting + Edge Density

0.10 0.15 0.20 0.25 0.30 0.35 0.40

0.10 0.15 0.20 0.25 0.30 0.35 0.40

Experiment 3: Focus and Edge Density

False Success Rate

False Failure Rate

Baseline

Focus Only

Edge Density Only

Focus + Edge Density

0.10 0.15 0.20 0.25 0.30 0.35 0.40

0.10 0.15 0.20 0.25 0.30 0.35 0.40

Experiment 4: Lighting and Focus

False Success Rate

False Failure Rate

Baseline

Focus Only

Lighting Only

Lighting + Focus

Figure 3. This figure shows ROC plots comparing the relative predictive power of alternative GLMMs which include combinations of

covariates. Experiments 1 through 4 correspond to the conjectures 1 through 4 in 2. In the Experiment 2 plot the Lighting Only and

Lighting+Edge Density lie on top of one another.

and in the plot one sees the Lighting+Edge Density curve

nearly completely covering the Lighting Only curve. Also

note that both curves have a lower equal error rate than the

Edge Density Only curve. At least in this experiment, there

appears to be no additional predictive value associated with

knowing edge density once a good lighting estimate is available.

5.3. Experiment 3: Focus and Edge Density

Our conjecture was that a good measure of whether the

face is in focus would better predict failure than edge density. The ROC presented in Figure 3 suggests this is not the

case. Note the order, from lowest to highest equal error rate

is: Focus+Edge Density, Edge Density Only, Focus Only

and Baseline. In short, edge density as a measurable prop-
erty of the query face images is doing a slightly better job

of predicting recognition failure then is the newly proposed

focus measure. Also, since performance is slightly better

when both variables are used, it appears that each variable

is providing some independent information. Our results are

consistent with the possibility that focus itself is not a major

contributing factor to verification failure.

5.4. Experiment 4: Lighting and Focus

Our initial conjecture, based upon the fact that focus

and lighting are operationally distinct, was that predictions

based upon the two together would be better then either

alone. As shown in Figure 3, the result does not support this

conjecture. Instead, the two curves with lowest equal error rate are for Lighting Only and Lighting+Focus; the two

curves lie nearly on top of each other. Further, the error rate

for the Focus Only GLMM is much higher. Two things are

apparent from this result. First, the newly proposed lighting

model is doing a good job of predicting when verification

will fail. In fact, the information is good enough that the

introduction of the focus measure adds nothing. Second, in

order to make sense of the lack of improvement when focus

is added, we must again entertain the notion that focus is

unrelated to verification failure.

In this experiment one might ask whether a focus effect

is being masked by lighting. Theoretically, the effect of

focus on performance might depend on the nature of the

lighting, with the average effect across the lighting range

being close to zero. To test this, we fit a fifth model that included both predictors plus their interaction. The result still

showed no evidence that focus mattered. The same strategy

was also used in Experiment 2, again yielding a negative

finding.

5.5. Summarizing Effects

Beveridge et al. [3] reported effects in terms of estimated

probability of verification associated with individual covariates and combinations of covariates. Here we do the same

for the main effects of Lighting, SEMC Focus and Edge

Density. These effects are reported for the models developed for Experiments 2, 3 and 4. They are summarized in

Figure 4.

The vertical axis indicates the estimated probability that

verification will succeed. For SEMC Focus and Edge Density, the horizontal axis represents standardized covariate

values, where standardization in this context means the

original values have been scaled and shifted to have a sample mean of zero and a sample standard deviation of one.

The horizontal axis for Lighting was left in the original raw

units of the illumination model, and the four values shown

may be interpreted as follows: 0.05 indicates frontal lighting, −0.05 indicates modest side lighting, −0.10 indicates

notable side lighting, and −0.18 indicates very strong side

lighting. These three covariates were included in GLMMs

for multiple experiments, and so the main effects for distinct experiments are shown. When reporting probability of

verification estimates, all other covariates in the GLMM are

set to baseline (default) values.

Lighting has a very strong effect. Estimated probability

of verification for frontal lighted images is around 0.95 and

drops down to roughly 0.65 for the images where side lighting is most pronounced. The effect is nearly identical for the

Lighting+Edge Density (Exp2) and Lighting+Focus (Exp3)

GLMMs. By any standard, this is a large main effect.

The SEMC Focus and Edge Density main effects viewed

together tell an interesting story. First recall that Experiment 3 uses a GLMM including both SEMC Focus and

Edge Density, and does not include Lighting. Consequently,

a main effect associated with Experiment 3 is shown for

both SEMC Focus and Edge Density. Moreover, larger values of SEMC Focus are associated with increased probability of verification. This is consistent with common expectation; recognition is easier when query images have a higher

focus score. In contrast, increased Edge Density in Experiment 3 is associated with decreased probability of verification.

Note, however, that the main effect for both SEMC Focus and Edge Density essentially disappears when each is

paired with Lighting, as in Experiments 4 and 2 respectively. This is further evidence that Lighting is a highly

predictive covariate, and that when added to a GLMM, it

subsumes much of what is otherwise attributed to SEMC

Focus and Edge Density.

6. Conclusions

Prior work showed that face recognition on uncontrolled

images remains challenging and that high edge density in

images predicts failure. The study presented here introduces a new model for measuring illumination direction in

images and shows that side lighting strongly degrades algorithm performance. In fact, the new lighting measure

subsumes and explains in physical terms the previously reported edge density effect. More generally, we show that

lighting remains a problem for state-of-the-art algorithms,

circa FRVT 2006. Consequently, lighting direction can be

viewed as an important quality measure that predicts face

recognition performance.

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