COMPARISON OF FOCUS MEASURES IN FACE DETECTION

ENVIRONMENTS

J. Lorenzo, O. D

eniz, M. Castrill

on and C. Guerra

Inst. of Intelligent Systems and Numerical Appl. in Engineering (IUSIANI)

University of Las Palmas de Gran Canaria, Campus Univ. de Taﬁra, 35017 Las Palmas, Spain

Campus Univ. de Taﬁra, 35017 LAs Palmas, Spain

Keywords:

Human-Computer Interaction, Computer Vision, Autofocus measures.

Abstract:

This work presents a comparison among different focus measures used in the literature for autofocusing in a

non previously explored application of face detection. This application has different characteristics to those

where traditionally autofocus methods have been applied like microscopy or depth from focus. The aim of the

work is to ﬁnd if the best focus measures in traditional applications of autofocus have the same performance

in face detection applications. To do that six focus measures has been studied in four different settings from

the oldest to more recent ones.

1 INTRODUCTION

In face detection and face recognition methods (Pent-

land et al., 1994; Rowley et al., 1998; Gross et al.,

2001; Hjelmas and Low, 2001; Yang et al., 2002;

Zhao et al., 2003), borders play an important role be-

cause they deﬁne the facial features that appear in the

face such as eyes, mouth and nose, which are needed

to carry out the task. In a blurred image these facial

features are not well deﬁned, and so the detection or

identiﬁcation can not be done. Blurred images can

be obtained in a defocused camera because defocus-

ing can be modelled as a low-pass ﬁltering process,

opposite to focused images which have a higher fre-

quency content (Nayar, 1994). Thus, it is desirable

that the image acquisition system has an autofocus

mechanism.

Automatic focusing methods fall into two main

categories: active and passive systems. Active meth-

ods are based on emiting a wave in order to estimate

the distance to the object of interest and consequently

adjust the lens position. Passive methods estimate the

position of the lens by means of ﬁnding the position

where image sharpness is maximum. Passive focus-

ing methods are normally found in low cost consumer

digital cameras, while active focus is widely used in

professional digital cameras and computer vision ap-

plications such as microscopy or inspection.

As mentioned above, passive autofocusing meth-

ods adjust the focus lens to maximize the high fre-

quency components in the image. Therefore a focus

value is needed to measure the amount of high fre-

quencies in the image. As autofocusing is a long-

standing topic, in the literature a wide variety of focus

algorithms have been proposed each one with an as-

sociated focus measure (Krotkov, 1987; Nayar, 1994;

Lee et al., 1995; Subbarao and Tyan, 1998; Choi and

Ki, 1999; Lee et al., 2001; Nathaniel et al., 2001; Ke-

htarnavaz and Oh, 2003; Kristan and Pernus, 2004;

Shirvaikar, 2004; Park and Kim, 2005; Kristan et al.,

2006).

Most of the published autofocusing algorithms

solve the problem of planar objects like in microscopy

applications (Sun et al., 2004) or single object of

interest like depth from focus applications (Nayar,

1994). In these applications, focus measures exhibit

an ideal curve with a peak with step slopes at the lens

position where the object is focused because there is

only one object in the image or because it is a pla-

nar image. However, in human computer interaction,

people do not always hold the same position in the im-

age and exists more objects in the scene so the focus

measure does not exhibit a clear maximum. In dig-

ital photograhy this drawback is eluded because the

photographer selects the object of interest and centers

it.

418

Lorenzo J., Déniz O., Castrillón M. and Guerra C. (2007).

COMPARISON OF FOCUS MEASURES IN FACE DETECTION ENVIRONMENTS.

In Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics, pages 418-423

DOI: 10.5220/0001644604180423

 SciTePress

In this work we analyze the performance of some

focus measures in different scenarios including typi-

cal human computer interaction. These measures are

explained in Section 2. Section 3 describes the exper-

iments and ﬁnally in Section 4 the results achieved are

commented.

2 FOCUS ALGORITHMS

As explained in the previous section, many focus

measures have been proposed in the last years to solve

the autofocus problem. All of them rely on the fact

that a focused image has high contents of higher fre-

quencies so any measure which computes these fre-

quencies can be used. In this work, six of these mea-

sures have been chosen to make the comparison. We

have compared well known focus measures with more

recent ones. Below, we brieﬂy describe each one.

The Tenenbaum Gradient (Tenengrad) (Krotkov,

1987) was one of the ﬁrst proposed focus measures.

This measure convolves the image with vertical (S

)

and horizontal (S

) Sobel operators. To get a global

measure over the whole image, the square of the gra-

dient vector components are summed.

T enengrad

(x, y)

+ S

(x, y)

(1)

The entropy measure proposed by Firestone et al.

(Firestone et al., 1991) is based on the idea that the

histogram of a focused image contains more informa-

tion than the histogram of a defocused one. In this

measure the histogram is normalized to get the prob-

ability p(i) for each gray level i.

Entropy

= −

intensities

p(i) log p(i) (2)

The Sum of Modiﬁed Laplace (SML) (Nayar,

1994) is based on the linear differential operator

Laplacian which has the same properties in all direc-

tions and is therefore invariant to rotation. Thus, the

SML measure sums the absolute values of the convo-

lution of the image with the Laplacian operators.

SM L

(x, y)| + |L

(x, y)| (3)

Energy Laplace (Subbarao and Tyan, 1998) is

based on the same idea of the SML mesasure but the

image is convolved with the following mask,

L =





−1 −4 −1

−4 20 −4

−1 −4 −1





which computes the second derivate D(x, y) of the

image. The value of the focus measure is the sum of

the squares of the convolution results.

EnergyLaplace

D(x, y)

(4)

Figure 1: Printed circuit board image.

Figure 2: Box picture.

Nanda and Cutler (Nanda and Cutler, 2001) pro-

posed a focus measure from the contrast of a image as

the absolute difference of a pixel with its eight neigh-

bors, summed over all the pixels of the image.

Contrast

C(x, y) (5)

where the contrast C(x, y) for each pixel in the gray

image I(x, y) is computed as

C(x, y) =

x+1

i=x−1

y+1

j=y−1

|(I(x, y) − I(i, j)|

Kristan et al. (Kristan et al., 2006) described

, which is one of the most recent focus mea-

sures. It is based on the coefﬁcients of the discrete co-

sine transform obtained after dividing the image into

8x8 non overlapped windows and then averaging over

all the 8x8 windows. It must be noticed that in our

implementation we have no ﬁltered components cor-

responding to high order frequencies as Kristan pro-

poses.

′

num. of 8x8 windows

(6)

where M

′

is computed from the DCT coefﬁcients

F (ω, ν) as

′

= 1 −

|F (ω, ν)|

(

|F (ω, ν)|)

COMPARISON OF FOCUS MEASURES IN FACE DETECTION ENVIRONMENTS

419

Figure 3: First human computer interaction scenario.

Figure 4: Second human computer interaction scenario.

3 EXPERIMENTAL STUDY

The images were acquired with a Sony DFW-VL500

ﬁrewire color camera with a 16x integrated zoom

in an indoor environment and four different settings

were analyzed. The ﬁrst setting corresponds to a

printed circuit board which yields a planar image with

all the scene elements to the same distance of the cam-

era; we refer to this setting as PCB (Fig. 1). The

second setting is very common in depth from focus

applications where an isolated object appears in the

image, this setting will be refered as Box (Fig. 2).

The third and fourth settings are the ones that we typ-

ically found in a human computer interaction appli-

cation where a person appears either in front of the

camera or in an ofﬁce enviroment. They will be ref-

ered as Face1 (Fig. 3) and Face2 (Fig. 4).

As the camera has 450 focus positions, 224 im-

ages for each of the previosly described settings were

acquired with a 2 focus position step. For each ac-

quired image the six focus measures were computed

and the criterium to assess the quality of each mea-

sure was the similarity of the resulting curve with an

“ideal” focus curve which exhibits only a sharp peak.

Figure 5 shows the normalized curves of the

Tenengrad focus measure for the four examples. This

0 50 100 150 200 250 300 350 400 450

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Focus Measure Value

Focus Position

PCB

Box

Face1

Face2

Figure 5: Normalized curves for the Tenengrad focus mea-

sure.

0 50 100 150 200 250 300 350 400 450

0.4

0.5

0.6

0.7

0.8

0.9

Focus Measure Value

Focus Position

PCB

Box

Face1

Face2

Figure 6: Normalized curves for the entropy focus measure.

measure gives good results for PCB and Box exam-

ples because in both cases there is a well deﬁned max-

imum at positions 198 and 176 respectively. On the

other hand in the examples Face1 and Face2, the

obtained curve does not show a sharp peak so the

maximum search is more difﬁcult.

Figure 6 shows the normalized curves of the en-

tropy focus measure for the four examples. As it is

shown in the graphics, the behaviour of this measure

is not so good as Tenengrad measure. Entrogy based

measure only gives a good focus curve for the PCB

example with the maximum located at position 216.

For Face1 and Face2 examples the curve increases

its value until it reaches a plateau where a maximum

is really difﬁcult to ﬁnd.

The results of the SML measure in the four exam-

ples are shown in Figure 7. This measure exhibits a

well deﬁned peak in PCBand Boxwith maxima at po-

sitions 200 and 180 respectively. In relation to Face1

example, we get a curve with with a maximum at 246

although the peak is not so sharp as in PCB and Box

examples. In example Face2 the resulting curve for

the SML measure has a ﬂattened peak with the maxi-

mum located at 368.

ICINCO 2007 - International Conference on Informatics in Control, Automation and Robotics

420

Table 1: Maximum of each focus measure.

Example F

T enengrad

Entropy

SM L

EnergyLaplace

Constrast

Expert

PCB 200 216 200 200 200 200 202

Box 176 176 180 176 280 180 178

Face1 270 290 246 240 272 264 240

Face2 362 108 368 362 334 284 330-370

0 50 100 150 200 250 300 350 400 450

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Focus Measure Value

Focus Position

PCB

Box

Face1

Face2

Figure 7: Normalized curves for the Sum of Modiﬁed

Laplacian focus measure.

0 50 100 150 200 250 300 350 400 450

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Focus Measure Value

Focus Position

PCB

Box

Face1

Face2

Figure 8: Normalized curves for the Energy Laplace focus

measure.

Energy Laplace and SML measures are based on

the Laplacian of the image because it gives a high re-

sponse to the higher frequencies in the image. Thus

the results we get with Energy Laplace are very simi-

lar to those obtained with SML measure as it is shown

in Figure 8. For PCB example the position of the max-

imum is the same than in the SML measure. In Box

example the maximum is at position 176 while for the

SML is in position 180, which are very close. The dif-

ference in focus position of this measure and SML in

examples Face1 and Face2 is 6 steps in both cases

that conﬁrms the similar behavior of both measures.

As shown in Figure 9, the measure proposed by

Nanda and Cutler gives similar results to those ob-

0 50 100 150 200 250 300 350 400 450

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Focus Measure Value

Focus Position

PCB

Box

Face1

Face2

Figure 9: Normalized curves for the focus measure pro-

posed by Nanda and Cutler.

0 50 100 150 200 250 300 350 400 450

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Focus Measure Value

Focus Position

PCB

Box

Face1

Face2

Figure 10: Normalized curves for the M

focus measure.

tained with the previously analyzed measures (except

entropy) in examples PCB and Box. For Face1 and

Face2, the results are similar too because in neither

cases a sharpened peak appears at position of maxi-

mum which is located at 272 and 334 respectively.

Finally, the results obtained with the M

mea-

sure are shown in Figure 10. As one of the most re-

cent focus measures it is expected to achieve the best

results in the four settings. However, for the PCB and

Box examples, the obtained results are very similar

to the other measures even worse than more classi-

cal measures as SML or Tenengrad. In Face1 and

Face2, the results are not so good as other measures

because the curves does not exhibit a sharp peak in

the maximum value so more elaborated methods for

COMPARISON OF FOCUS MEASURES IN FACE DETECTION ENVIRONMENTS

421

the maximum location are needed.

The previous analisys only takes into account the

shape of the focus curve but to assess the quality of

the lens position which corresponds to the maximum

it need to be tested by an expert because there is no

possibility to compare with a reference focus mea-

sure. Sun et al. (Sun et al., 2004) propose as reference

to test the accuracy of the compared methods the dif-

ference between the lens position given by the method

and the lens position selected by an expert.

In this work we have followed a similar approach

and for each scenario an expert was asked for getting

the most focused image. The range of focus value

are shown in the column labelled as Expert of ta-

ble 1. To notice that for the Face2 scenario the

range of focus values in which the image is focused is

wider because the person is further and so the depth

of ﬁeld is larger. Comparing in Table 1, the best focus

value given by each measure and the one selected by

the expert, it is observed that the most accurate mea-

sures are those based on Laplacian as SML and En-

ergy Laplace. The others exhibit a similar accuracy

except Entropy that as in the previous analysis about

the shape of the curve exhibits the worst accuracy.

4 CONCLUSIONS

In this work a comparison of six focus measures have

been carried out to investigate the performance of the

measures in a face detection application. In face de-

tection applications the person, which is the object of

interest, normally is in an ofﬁce environment so the

obtained curves do not exhibit a sharp peak at one de-

ﬁned focus position. Instead, ﬂattened peaks are ob-

tained which make more difﬁcult to get the best focus

position. From the six compared focus measures, all

of them, except the entropy measure, give very similar

results in non face detection applications. In the two

face detection settings the best results were obtained

with SML and Energy Laplace measures and surpris-

ingly the most recently published measure does not

give as good results as previous ones. So we have

concluded that for face detection applications the best

performance is obtained with Laplacian based mea-

sures but it is necessary to use more elaborated max-

imum ﬁnding methods because there does not exist

very sharp peaks in the focus curves. Also, a test

about the accuracy of the focus position was carried

out, using as reference the focus position given by an

expert for each example. The results are very similar

to the previous given as the two most accurate mea-

sures those based on Laplacian and the worst accuracy

the Entropy measure.

ACKNOWLEDGEMENTS

This work has been partially supported by the Spanish

Ministry of Education and Science and FEDER funds

under research project TIN2004-07087.

REFERENCES

Choi, K.-S. and Ki, S.-J. (1999). New autofocusing tech-

nique using the frequency selective weighthed median

ﬁlter for video cameras. IEEE Transactions on Con-

sumer Electronics, 45(3):820–827.

Firestone, L., Cook, K., Culp, K., Talsania, N., and Preston,

K. (1991). Comparison of autofocus methods for au-

tomated microscopy. Cytometry, 12:195–206.

Gross, R., Shi, J., and Cohn, J. (2001). Quo vadis face

recognition? - the current state of the art in face

recognition. Technical Report CMU-RI-TR-01-17,

Robotics Institute, Carnegie Mellon University.

Hjelmas, E. and Low, B. K. (2001). Face detection: A

survey. Computer Vision and Image Understanding,

83(3):236–274.

Kehtarnavaz, N. and Oh, H.-J. (2003). Development and

real-time implementation of a rule-based auto-focus

algorithm. Real-Time Imaging, 9:197–203.

Kristan, M. and Pernus, F. (2004). Entropy based measure

of camera focus. In Proceedingsof the thirteenth Elec-

trotechnical and Computer Science Conference ERK,

pages 179–182.

Kristan, M., Pers, J., Perse, M., and Kovacic, S. (2006). A

bayes-spectral-entropy-based measure of camera fo-

cus using a discrete cosine transform. Pattern Recog-

nition Letters, 27(13):1419–1580.

Krotkov, E. (1987). Focusing. International Journal of

Computer Vision, 1:223–237.

Lee, J.-H., Kim, K.-S., and Nam, B.-D. (1995). Implemen-

tation of a passive automatic focusing algorithm for

digital still camera. IEEE Transactions on Consumer

Electronics, 41(3):449–454.

Lee, J.-S., Jung, Y.-Y., Kim, B.-S., and Sung-Jea, K. (2001).

An advanced video camera system with robust AF,AE

and AWB control. IEEE Transactions on Consumer

Electronics, 47(3):694–699.

Nanda, H. and Cutler, R. (2001). Practical calibrations for a

real-time digital onmidirectional camera. In Proceed-

ings of the Computer Vision and Pattern Recognition

Conference (CVPR 2001).

Nathaniel, N., Neow, P., and Ang, M. (2001). Practical is-

sues in pixel-based autofocusing for machine vision.

In Proc. of the 2001 IEEE International Conference

on Robotics and Automation, Seoul, Korea.

Nayar, Shree K.and Nakagawa, Y. (1994). Shape from fo-

cus. IEEE Transactions on Pattern Analysis and Ma-

chine Intelligence, 16(8):824–831.

Park, R. K. and Kim, J. (2005). A real-time focusing algo-

rithms for iris camera recognition. IEEE Transactions

on Systems, Man and Cybernetics, 35(3):441–444.

ICINCO 2007 - International Conference on Informatics in Control, Automation and Robotics

422

Pentland, A., Moghaddam, B., and Starner, T. (1994).

View-based and modular eigenspaces for face recog-

nition. In Proc. of IEEE Conf. on Computer Vision

and Pattern Recognition (CVPR’94), Seattle, WA.

Rowley, H. A., Baluja, S., and Kanade, T. (1998). Neural

network-based face detection. IEEE Transactions on

Pattern Analysis and Machine Intelligence, 20(1):23–

38.

Shirvaikar, M. (2004). An optimal measure for camera fo-

cus and exposure. In Proceedings of the Thirty-Sixth

Southeastern Symposium on System Theory, pages

472– 475.

Subbarao, M. and Tyan, J.-K. (1998). Selecting the optimal

focus measure for autofocusing and depth-from-focus.

IEEE Transactions on Pattern Analysis and Machine

Intelligence, 20(8):864–870.

Sun, Y., Duthaler, S., and Nelson, B. J. (2004). Autofocus-

ing in computer microscopy: Selecting the optimal fo-

cus algorithm. Microscopy Research and Technique,

65:139–149.

Yang, M., Kriegman, D., and Ahuja, N. (2002). Detecting

faces in images: A survey. IEEE Transactions on Pat-

tern Analysis and Machine Intelligence, 24(1):34–58.

Zhao, W., Chellappa, R., Rosenfeld, A., and Phillips, P.

(2003). Face recognition: A literature survey. ACM

Computing Surveys, pages 399–458.

COMPARISON OF FOCUS MEASURES IN FACE DETECTION ENVIRONMENTS

423