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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 2, May 2010
Preprocessing of video image with unconstrained
background for Drowsy Driver Detection
M.Moorthi1, Dr. M.Arthanari2, M.Sivakumar3
1
Assistant Professor, Kongu Arts and Science College, Erode – 638 107, Tamil Nadu, India
2
Prof. & Head, Tejaa Sakthi Institute of Technology for Women, Coimbatore – 641 659, Tamil Nadu, India
3
Doctoral Research Scholar, Anna University, Coimbatore, Tamil Nadu, India
Email: moorthi_bm_ka@yahoo.com, arthanarimsvs@gmail.com, , Email: sivala@gmail.com
Abstract of the problem and illumination changes, robustness and
preprocessing steps of these approaches are still a problem.
The face recognition includes enhancement and Most commonly, natural face feature templates taken from real
segmentation of face image, detection of face boundary and facial person are used for a template matching algorithm [1],[2].
features, matching of extracted features, and finally recognition of the These templates have to satisfy a set of requirements like
face. Though a number of algorithms are devised for face recognition, orientation, size and illumination. Therefore preprocessing step
the technology is not matured enough to recognize the face of a person
is necessary for at least aligning and size changes. A wavelets
since the algorithm deal with significant amount of illumination
based approach is described in [3]. Face images and face
variation in image. We propose a new image preprocessing algorithm
features from a database have to be aligned in orientation and
that compensates for the problem. The proposed algorithm enhances
the contrast of images by transforming the values in an intensity size in preprocessing step. Both previous described methods
image, so that the histogram of the output image is approximately are limited by the used template and face database.
uniformly distributed on pixel. Our algorithm does not require any
training steps or reflective surface models for illumination In this paper we propose a novel low cost method
compensation. We apply the algorithm to face images prior to designed for preprocessing. The preprocessing has three steps.
recognition. Simulation is done using seventy five web camera images In first steps modified histogram equalization is used to
using Mat lab 7.0.
enhance the brightness and contrast of the images. In steps two,
median filter is used to remove salt and pepper noise. Third,
Keywords: Facial recognition, Facial features extraction, Eye
Binary image are obtained through the thresholding.
detection
1. Introduction
This paper is organized as follows. Literature surveys
The preprocessing of real image is a crucial aspect in
are given in section 2. In section 3 we will devote ourselves to
many useful applications like video coding of faces for video
discussing the preprocessing method in detail. Experimental
phony, animation of synthetic faces, driver behaviors analysis,
results are reported in section 4. Conclusions will be drawn in
word visual recognition, expression and emotion analysis,
section 5.
tracing and recognition of faces. The detection of facial
features has been approached by many researchers and a
variety of methods exist. Nevertheless, due to the complexity
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2. Literature Survey enhanced method of histogram equalization is used to
preprocess the image.
Besides pose variation, illumination is the most
significant factor affecting the appearance of faces. Ambient 3. Preprocessing
lighting changes greatly within and between days and among In order to obtain appropriately-segmented binary
indoor and outdoor environments. Due to the 3D shape of the images, an image preprocessing is applied. To compensate for
face, a direct lighting source can cast strong shadows that illumination variations and to obtain more image details,
accentuate or diminish certain facial features. Evaluations of modified histogram equalization is used to enhance the
face recognition algorithms consistently show that state-of-the- brightness and the contrast of the images. Then a median filter
art systems can not deal with large differences in illumination is used to remove the noise. Binary images are obtained
conditions between gallery and probe images [1-3]. through thresholding. The preprocessing steps are shown in Fig
1.Let we see the steps of preprocessing one by one.
The face detection algorithms are based on either gray
level template matching or computation of geometric 3.1. Capturing image
relationships among facial features. In recent years many The required images are taken from the video image
appearance-based algorithms have been proposed to deal with using web camera.
the problem [4-7]. Belhumeur showed [5] that the set of images 3.2 Enhancing the image
of an object in fixed pose but under varying illumination forms Histogram equalization is a method in image
a convex cone in the space of images. The illumination cones processing of contrast adjustment using the image's histogram.
of human faces can be approximated well by low-dimensional This method usually increases the global contrast of many
linear subspaces [8]. The linear subspaces are typically images, especially when the usable data of the image is
estimated from training data, requiring multiple images of the represented by close contrast values. Through this adjustment,
object under different illumination conditions. Alternatively, the intensities can be better distributed on the histogram. This
model-based approaches have been proposed to address the allows for areas of lower local contrast to gain a higher contrast
problem. Blanz et al. [9] fit a previously constructed morphable without affecting the global contrast. Histogram equalization
3D model to single images. The algorithm works well across accomplishes this by effectively spreading out the most
pose and illumination, however, the computational expense is frequent intensity values.
very high.
The method is useful in images with backgrounds and
In general, an image I(x; y) is regarded as product I(x; foregrounds that are both bright or both dark. A key advantage
y) = R(x; y)L(x; y) where R(x; y) is the reflectance and L(x; y) of the method is that it is a fairly straightforward technique and
is the illuminance at each point (x; y) [10]. Computing the an invertible operator. So in theory, if the histogram
reflectance and the illuminence fields from real images is, in equalization function is known, then the original histogram can
general, an ill-posed problem. Therefore, various assumptions be recovered. The calculation is not computationally intensive.
and simplifications about L, or R, or both are proposed in order
to attempt to solve the problem. A common assumption is that Histogram equalization often produces unrealistic
L varies slowly while R can change abruptly. For example, effects in photographs; however it is very useful for scientific
Homomorphic filtering [11] uses this assumption to extract R images like thermal, satellite or x-ray images, often the same
by high-pass filtering the logarithm of the image. In this paper, class of images that user would apply false-color to
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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 2, May 2010
contrast. Examples of such methods include adaptive histogram
Input equalization and contrast limiting adaptive histogram
Video equalization.
image
Histogram equalization also seems to be used in
Enhancing
video biological neural networks so as to maximize the output firing
image rate of the neuron as a function of the input statistics. This has
Median been proved in particular in the fly retina. Histogram
filtering equalization is a specific case of the more general class of
histogram remapping methods. These methods seek to adjust
the image to make it easier to analyze or improve visual quality
Thresholding
3.3. Proposed Modification
Binary
image
While the results of a standard histogram equalization
filtering over the whole image just described give promising
Fig. 1: Preprocessing steps.
results, we wanted to see if the results could be further
. Also histogram equalization can produce undesirable improved. Many well-known enhancement algorithms such as
effects (like visible image gradient) when applied to images histogram equalization and homomorphic filtering are global in
with low color depth. For example if applied to 8-bit image nature and are intended to enhance an image and deal with it as
displayed with 8-bit gray-scale palette it will further reduce a whole. We tried to split the original image in sub-images and
color depth (number of unique shades of gray) of the image. filter each sub-image individually. First we decided to try and
Histogram equalization will work the best when applied to split the image into two halves vertically (thus obtaining two
images with much higher color depth than palette size, like sub-images of the original image) and then apply the filter to
continuous data or 16-bit gray-scale images. each half individually. Second idea was to split the image
horizontally and again apply the filter to each half individually.
There are two ways to think about and implement Encouraged by the good results obtained with both these
histogram equalization, either as image change or as palette methods (see Section 4 for details) we further tried to combine
change. The operation can be expressed as P(M(I)) where I is the filtering results into a joint representation. Let IHEV(x,y) be
the original image, M is histogram equalization mapping the image split vertically and each half filtered with histogram
operation and P is a palette. If we define new palette as equalization filter individually, let IHEH(x,y) be the same for
P'=P(M) and leave image I unchanged then histogram horizontally split images and let IHEMOD(x,y) be our proposed
equalization is implemented as palette change. On the other modification:
hand if palette P remains unchanged and image is modified to
I'=M(I) then the implementation is by image change. In most IHEMOD(x, y) = 0.5[ IHEV(x, y) + .70 IHEH(x,y)]
cases palette change is better as it preserves the original data.
Since IHEV scored higher results than IHEH in our
Generalizations of this method use multiple tests we decided to keep the whole IHEV and multiply IHEH
histograms to emphasize local contrast, rather than overall with a constant of 0.70 (chosen based on experimental results),
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to lower its influence on the final representation. This eliminated by the conventional connected components labeling
combination produced highest results in our experiments and process.
was kept as a final representation. We will show in the
following section that our method yields superior results, and 3.6 Binary image
therefore justifies further research of the histogram equalization Binary images are obtained through the thresholding.
filtering variations as a means of simple yet efficient image Then the final feature image is obtained, as shown in Fig. 4 (c).
preprocessing.
4. Experimental Results
As shown in Fig. 4 (a), the input image has low The proposed method was tested on the real video
contrast due to illumination; segmentation results, therefore, images. The video image of [480 x 640 pixels] of 75 different
are unlikely to be good. Fig. 4 (b) demonstrates the image test persons and has been recorded during several sessions at
enhanced by modified histogram equalization the contrast is different places. This set features a larger variety of
improved, and the details in the face region are enhanced which illumination, background and face size. It stresses real world
are discussed in detail in the following section. constraints. So it is believed to be more difficult than other
datasets containing images with uniform illumination and
3.4 Median filtering background. The facial image can be preprocessed successfully
in most cases, no matter whether face patterns are in different
The intensity in the eye region and other facial scale, expression, and illumination conditions. Typical results
features is dark in a grey-level facial image. The image has of preprocessing with the proposed approach are shown in
been enhanced through modified histogram equalization. In Fig.4. The input images vary greatly in background, scale,
image processing it is usually necessary to perform a high expression and illumination, the images also including partial
degree of noise reduction in an image before performing face occlusions and glasses wearing.
higher-level processing steps, such as edge detection. The
median filter is a non-linear digital filtering technique, often 4.1 Method Tested
used to remove noise from images or other signals. No enhancement (NE). For this test we only
geometrically normalized the images (actually, images were
Median filtering is a common step in image
geometrically normalized in all subsequent tests as well). No
processing. It is particularly useful to reduce speckle noise and
filtering or histogram equalization is used.
salt and pepper noise. Its edge-preserving nature makes it
useful in cases where edge blurring is undesirable.
Standard histogram equalization (HE): Images were
geometrically normalized and a standard histogram
3.5 Thresholding
equalization (HE) technique was employed. HE enhances the
contrast of images by transforming the values in an intensity
After median filtering threshold is set to 128, so that
image, so that the histogram of the output image is
only dark pixels remain, including eye pair structure. Then, a
approximately uniformly distributed on pixel intensities of 0 to
binary image is obtained, which obviously contains the facial
255.
structure. Taking into account that the nonface area can
influence the speed and the results of template matching, the
HE vertical (HEV): Histogram equalization filtering of two
oversize black area, which is useless in the binary image, is
sub images are obtained by vertically dividing the input image
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into two halves prior to filtering and then filtering each of
them. The resulting image is obtained by concatenating the two The fig 4 shows the implementation results of image preprocessing.
filtered halves. The fig 4.a and fig 4.b are the results of enhanced image and binary
HE horizontal (HEH): The same procedure as in HEV is used image. By looking at the extremely low recognition rate on NE images
just 4.15%, the proposed method is better. In our experiment the
with the exception of an image being horizontally divided.
standard preprocessing HE which yielded only 48.20%. HEV and
HEMOD give significant improvement with 60% and 60.20%
HE modified (HEMOD): Method proposed in Section 3.3,
respectively. Therefore, we can see clearly that our proposed method
consisting in combining results from HEV and HEH is superior to all other methods and recognition rate is 12% is higher
than the standard HE. The superiority of the proposed method is
Table 1, Results of applying all the techniques on further confirmed in Fig. 3 where the cumulative match score curve
video images The numbers in the table represent rank 1 for the standard method and proposed method could seen.
recognition rate (RR) in percentages of correctly recognized
images over the whole probe set.
Method NE HE HEV HEH HEMOD
RR % 4.15 48.20 60 58.30 60.20 (a) (b) (c)
Table 1: Recognition rate in percentages
4.2 Results
The fig. 2 shows the proposed preprocessing method gives
better results for finding the correct eye than other method
since the recognition rate of the eye here is 60.2%. (a) (b) (c)
Ey e R e c ogn i t i on R a t e Fig. 4: An example of preprocessing (a) Original Image (b)
70
Enhanced image (c) Binary image
60 60 58.3 60.2
50 48.2
40
30 5. Conclusions
20
10
4.15
0
NE HE HEV HEH HEMOD
M e t hods We introduced a simple image-preprocessing
algorithm for compensation of illumination variations in
Fig. 2 Comparison of various methods images. The algorithm enhances the contrast of images by
transforming the values in an intensity image so that the
90 histogram of the output image is approximately uniformly
85
80
distributed on pixel intensities. The algorithm delivers large
e o nt o ae
Rc g i i nRt
75
70
65 performance improvements for standard face recognition
60
55
50
HE
HEM OD algorithms. Experiments demonstrated the robustness of the
45
1 2 3 4 5 6 7 8 9 10 method with several images captured from web camera.
Rank
Fig. 3 Cumulative Match score curves for HE and proposed
method
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References
1. Phillips, P., Moon, H., Rizvi, S., Rauss, P.: The FERET
evaluation methodology for Face recognition Mr. M. Moorthi received MCA degree from Bharathiar
algorithms. IEEE PAMI 22 (2000) 1090-1104 University, Coimbatore, and M.Phil., Degree from
2. Blackburn, D., Bone, M., Philips, P.: Facial Manonmaniam Sundaranar University and doing PhD Degree
recognition vendor test 2000: evaluation report in R&D centre, Bharathiar University, Coimbatore, TN, India
(2000) . He is currently the Lecturer (SG) in Kongu Arts and Science
3. Gross, R., Shi, J., Cohn, J.: Quo vadis face College, Erode, TN, India. He has 11 years of teaching and
recognition? In: Third Workshop on Empirical 6 years of research experience. He has guided nine M.Phil
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4. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: papers in National and International Conference and has
Eigen faces vs. Fisherfaces: Recognition using class published an article in National Journal. He is a member of
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711-720 & Development Center of Science and Cultures – Advances in
5. Belhumeur, P., Kriegman, D.: What is the set of images Natural Science and Management Engineering – ISSN 1913-
of an object under all possible lighting 0341.His interests and expertise are in the area of Image
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6. Georghiades, A., Kriegman, D., Belhumeur, P.: From Networks.
few to many: Generative models for recognition under E-mail ID: moorthi_bm_ka@yahoo.com.
variable pose and illumination. IEEE PAMI (2001)
7. Riklin-Raviv, T., Shashua, A.: The Quotient image:
class-based re-rendering and recognition with
varying illumination conditions. In: IEEE PAMI. (2001)
8. Georghiades, A., Kriegman, D., Belhumeur, P.:
Illumination cones for recognition under variable Dr Arthanari holds a Ph.D in Mathematics from Madras
lighting: Faces. In: Proc. IEEE Conf. on CVPR. (1998) University as well as Masters degree in Computer Science from
9. Blanz, V., Romdhani, S., Vetter, T.: Face BITS, Pilani. He holds patent issued by the Govt. of India for
identification across different poses and the invention in the field of Computer Science. He has directed
illumination with a 3D morphable model. In: IEEE Conf. teams of Ph.D researchers and industry experts for developing
on Automatic Face and Gesture Recognition. (2002) patentable products. He teaches strategy, project management,
10. Horn, B.: Robot Vision. MIT Press (1986) creative problem solving, innovation and integrated new
11. Stockam, T.: Image processing in the context of a visual product development for last 35 years.
model. Proceedings of the IEEE 60 (1972) 828-842
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“Nonlinear filtering of multiplied and convolved signals”
IEEE Proc., vol. 56, no. 8, pp. 1264–1291, 1968.
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Mr. Sivakumar M (sivala@gmail.com) has 10+ years of
experience in the software industry including Oracle
Corporation. He received his Bachelor degree in Physics and
Masters in Computer Applications from the Bharathiar
University, India. He holds patent for the invention in
embedded technology. He is technically certified by various
professional bodies like ITIL, IBM Rational Clearcase
Administrator, OCP - Oracle Certified Professional 10g and
ISTQB.
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