Image Enhancement Technique using Adaptive
Multiscale Retinex for Face Recognition Systems
Khairul Anuar Ishak
1
, Salina Abdul Samad
1
M. A. Hannan
1
and Maizura Mohd Sani
2
1
Dept. of Electrical, Electronics and Systems Engineering
Faculty of Engineering and Built Environment, University Kebangsaan Malaysia
43600, UKM Bangi, Selangor, Malaysia
2
Institute of Microengineering and Nanoelectronics, University Kebangsaan Malaysia
43600, UKM Bangi, Selangor, Malaysia
Abstract. Various illumination effects in an image are one of the states of
difficulty that should be solved in order to get a satisfactory result in face
recognition task. The inhomogeneous intensities of the image has led to many
plans and algorithms to devastate the cause and next to eliminate the
illumination. The focus of this paper is to enhance the image by reducing
illumination effects; employing a preprocessing step i.e. adaptive multiscale
retinex as the illumination correction method before accomplishing the
recognition task. The performance of this method is evaluated using the Yale
database and has lower equal error rate compared with single scale retinex and
conventional multiscale retinex.
1 Introduction
In face recognition, usually there are some inconsistencies between the real scenes
and the training set images. One of them is illumination variations such as shadow,
blur, dark and noise occurring in the images. Sometimes this can cause degradation in
the algorithm to recognize the face image. In this paper, we want to reduce the
unwanted effects in face images by applying adaptive multiscale retinex as a
preprocessing step. Multiscale retinex was initially used to provide stability in color
images; however it is also competent to be used in gray scale images.
Lightness and color uniformity refer to wide range of intensity and spectral
illumination variations [1]. Multiscale retinex is formed from the retinex theory by
Edwin Land [2]. Land proposed the idea of retinex as a model of lightness to measure
the lightness response in an image.
However Land did not apply the model to image enhancement algorithm, but this
is done by Jobson where they define the properties of the surround/center retinex
function [3]. The characteristic they describe is single scale retinex when they
performed logarithmic after the surround function. They also apply ‘canonical’ gain
offset to the retinex output to clip certain parts of the highest and lowest signal
excursion. However, single scale retinex can either provide dynamic range
Ishak K., Abdul Samad S., Hannan M. and Mohd Sani M. (2009).
Image Enhancement Technique using Adaptive Multiscale Retinex for Face Recognition Systems.
In Proceedings of the 5th International Workshop on Artificial Neural Networks and Intelligent Information Processing, pages 43-49
DOI: 10.5220/0002262600430049
Copyright
c
SciTePress
compression on small scale, or tonal rendition for large scale image. This limitation
expands single scale retinex to a more balanced method that is multiscale retinex.
After that, another characteristic of multiscale retinex were found where other than
dynamic range compression, multiscale retinex purposes are to replicate tone in an
image in order to reduce its dependencies in lighting conditions and improved spatial
resolution[4]. So we use this characteristic in this paper to lessen illumination effects
in order to obtain controlled lighting condition in a face image. We modify the present
multiscale retinex by including histogram shifting and adaptive histogram
equalization to the original algorithm as to have a more uniform face image contrast
than the original method. The detail of this proposed method will be discussed in
section 3. Before that, section 2 will cover the original theory of multiscale retinex.
Section 4 will describe the experimental results and lastly is the conclusion in Section
5.
2 The Original Multiscale Retinex
The original multiscale retinex essentially measure the intensity of an image and
estimate the illumination from the proportion of the local image mean intensity value.
By applying Gaussian filter, the image is smoothed at different weight and size in
order to find the mean of the image. To obtain the retinex output, the filtered image is
divided using the illuminated image (input). Then, logarithmic function is done to
compress dynamic range of images with large variations in pixel value [5] before the
image is reconstructed again using additive function.
The original multiscale retinex algorithm is obtained from single scale retinex [4]
as in (1):
R(x,y)=logI(x,y)-log[F(x,y)*I(x,y)] (1)
where I(x,y) is input image, R(x,y) is retinex output, F(x,y) is the Gaussian surround
function. Symbol * denotes convolution. Gaussian surround function is given by:
F
x,y
=K.e
-(x
2
+y
2
)/c
2
(2)
where c is Gaussian shaped surrounding space constant. The value of cis related to
visual angle in the direct observation which is determined through experiment. K is
selected such that:
F
x,y
dxdy=1 (3)
Until this stage the single scale retinex would only provide tone reproduction and
dynamic range compression at certain scale in an image. The image would have only
one of the important characteristics. Thus, to overcome this limitation, superposition
of different scale at certain weight would solve this problem as shown in (4), where
44
Nis number of scale, where R
ni
is different scale of single scale retinex. ω
n
is the
weight of each single scale retinex with equal value.
R
MSRi
= ω
n
N
n=1
R
ni
(4)
3 The Adaptive Multiscale Retinex
After applying the original multiscale retinex, we found that the image was too
dark. This meant that the image brightness and contrast needed to be altered. Thus we
modified the algorithm by applying a recombination with the original image.
According to [6], a method need to be applied to restore the information in different
regions to smoothen the global contras in the image according to
which region is
darker or brighter. The information here is, different intensity in different regions in
the original picture. For this reason, recombination is needed to restore the
information as in (5):
R
MSRi =
ω
n
N
n=1
R
ni
+ω
original
·log(original) .
(5)
After recombination with weighted original picture, adjustment is made on the
histogram by performing a constant shift which helps improve the entire global
brightness of the image. To shift the histogram is a simple task, where in the range of
0-255 the image pixels should be. In order to allocate the pixels in the range, we set
the initial maximum pixel (MinVal) as 0, and the minimum pixel (MaxVal) as 255.
Then we evaluate the entire image pixels one by one and update the new value
(NewVal) using (6). Prior to that, every pixel value (PixVal) has to be tested whether
it is higher than the MaxVal or lower than the MinVal. If the value is higher, then the
MaxVal will be the PixVal value and the similarly, MinVal if the value of PixVal is
lower than MinVal. These values are needed to find the new value which is compute
from
NewVal
Pixval-MinVal
MaxVal-MinVal
255.
(6)
Next, we execute a local image enhancement technique that divides the image into
rectangular blocks. Usually how many blocks should be used is determine through
experiments. First, obtain the cumulative density function of the small region
histogram. Then the centre pixel of the region is equalized (histogram equalization)
and moved to the adjacent pixel in the rectangular region. This process is called
adaptive histogram equalization (AHE) [7].
Overall, the adaptive multiscale retinex algorithm is shown as in figure 1.
45
Fig. 1. The adaptive multiscale retinex flowchart.
4 Experimental Results
We evaluate the performance of the preprocessing methods using Eigenface [8] as the
feature extractor while Euclidean distance is used for the matching purpose. We also
implement a fusion of Principal Component Analysis (PCA) and Linear Discriminant
Analysis (LDA) [9] for data reduction. The performance is evaluated using well
known benchmarking measures for biometrics system i.e. Equal Error Rate (EER)
[10]. To compute EER, two components must be determined. The first is false
acceptance rate (FAR), when the impostor is falsely regarded as the client. Another
one is false rejection rate (FRR), when the client is falsely regarded as the imposter.
Here the client is the authorize person in the face recognition system. The EER is the
cross-over value where FAR and FRR coincide.
The dataset we use to evaluate the algorithms is Yale [11], which contains 165
grayscale images of 15 individuals. There are 11 images per subject, one per different
facial expression or configuration. The 11 images show various extreme illuminations
and pose criteria. We randomly selected 5 images from each subject to be the training
sample and 5 images of each subject as the testing sample. Our experimental face
condition is cropped face images. The size of all images is standardized to 50x60.
The experiments are done using grayscale images as the inputs. Three methods are
compared, i.e., single scale retinex, multiscale scale retinex and the adaptive
multiscale retinex. Table 1 shows the EER for these methods where we can see that
the EER for the adaptive multiscale retinex is the lowest compared to the original
multiscale retinex and single scale retinex.
Table 1. EER comparison of three methods.
Method EER (%)
Single Scale Retinex 11.87
Multiscale Retinex 10.33
Adaptive Multiscale Retinex 10.27
Histogram
shifting
Original
multiscale retinex
Recombination
with logarithmic
function of
original image
Adaptive
Histogram
Equalization
46
The EER results correlate with the output images of the three methods. To
illustrate, we choose 4 faces from the database which have been illuminated with
different lighting conditions: cast shadow, attached shadow, specular reflection and
diffuse reflection [12]. The lighting conditions are shown in figure 2. Figure 3
illustrates the outputs for all three methods. For face number 1, only the adaptive
multiscale retinex is able to eliminate the diffuse reflection. For face number 2, there
are specular reflection and strong cast and attached shadow, where the adaptive
algorithm is capable in removing the specular reflection. The condition in face
number 3 is the same as number 1. Face number 4 contains cast shadow, specular and
diffuse reflection. The adaptive multiscale retinex is able to remove all the lighting
distraction on the face image, except the cast shadow.
Fig. 2. Different lighting conditions of a face.
Fig. 3. The image outputs using three methods for person 1, 2, 3, and 4.
Figure 4(a), (b), (c) and (d) show the output histograms for face images 1, 2, 3, and
4 before and after applying the adaptive multiscale retinex. For all the histograms, the
upper figure indicates the histogram before and the lower figure is the one after. All
the histograms show more balanced tone representation in gray scale values. The
histograms centred at the middle show ordinary conditions with peaks and gradually
tapering off on the left and right sides of the histogram. This proves that the method is
able to reduce broad tonal range from the original face image.
Cast shadow
Specular
reflection
Attached
shadow
Diffuse
reflection
1
2
3
4
Single Scale
Retinex
Ori
g
inal
Multiscale
Retinex
Adaptive Multiscale
Retinex
47
Before (a) After
Before (b) After
Before (c) After
Before (d) After
Fig. 4. (a), (b), (c), (d). The histogram before pre-processes (the original image) and after pre-
processed with adaptive multiscale retinex for person 1, 2, 3 and 4.
5 Conclusions
In this paper, an adaptive multiscale retinex algorithm is presented. The purpose is to
remove illumination appearances. This is achieved by modifying the multiscale
retinex algorithm with adaptive histogram equalization and histogram shifting. The
performance of this method is tested using the Yale dataset and shown in terms of
EER rate and output comparisons with single scale retinex and conventional
multiscale retinex.
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