Volume 6, Issue 12, December 2016 ISSN: 2277 128X …ijarcsse.com/.../Volume_6/12_December2016/V6I12-0149.pdf · 2017. 1. 3. · ( , ) u v C u v v N u M f x y D D » ¼ º «¬ ª

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Volume 6, Issue 12, December 2016 ISSN: 2277 128X

International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com

Comparative Analysis of Illumination Compensation with Different

Histogram Equilization Algorithms and Its Face Application Saba Tahseen (M.tech Student)

Dept. of Computer Science & Engineering,

Visvesvaraya Technological University,

Karnataka, India

Dr. Shubhangi D.C H.O.D of CS &Engineering,

Visvesvaraya Technological University,

Karnataka, India

Abstract— This paper proposes a face recognition and verification algorithms based on histogram equalization to

standardize the faces illumination reducing in such way the variations for further features extraction Illumination

compensation and normalization play a crucial role in face recognition. In this paper we are going to compare

histogram equalization algorithms. We claim that edge orientation is useful for face recognition. Three OLHE feature

combination schemes were proposed for face recognition: 1) encoded most edge orientations; 2) more compact with

good edge-preserving capability; and 3) performed exceptionally well when extreme lighting conditions occurred. The

proposed algorithm yielded state-of-the-art performance on AR, CMU PIE, and extended Yale B using standard

protocols. We further evaluated the average performance of the proposed algorithm when the images lighted

differently were observed, and the proposed algorithm yielded the promising results.

Keywords— Face recognition, illumination compensation, histogram equalization, extended yale database,Olhe

I. INTRODUCTION

FACE recognition remains a popular progress has been made, the difficulty posed by variations in illumination

or head orientation makes the task of general face recognition unsolved yet.

Biometrics consists of a set of automated methods for recognition or verification of individuals using physical or

behavioral characteristics of such; as face, fingerprint, signature, voice, etc. This technology is based on the fact that each

single person is unique and has distinctive features that can be used for identification Figure 1 shows the block diagram

of biometrics .

Fig 1. Block diagram illustrating different modules in a biometric system.

As shown in Fig. 2, different biometric modalities have been used in several applications based on its suitability

and user convenience. Fingerprint, face and iris are the most widely used biometric modalities. Other biometric

modalities that have gained sufficient popularity includes gait, hand geometry, palm-prints, and voice. In general, there is

no single biometric modality that is best for all applications. Moreover, in some applications, more than one biometrics is

used to attain higher security and to address failure to enroll. Such systems are called multimodal biometric systems.

Several factors have to be considered while designing any biometric system such as location, security risks, identification

or verification task, expected number of users and their characteristics. This section provides the review of some of the

most widely used and accepted biometric modalities.

Fig. 2. Biometric modalities that are widely used in several applications.

http://www.ijarcsse.com/

Tahseen et al., International Journal of Advanced Research in Computer Science and Software Engineering 6(12),

December- 2016, pp. 106-114


Face recognition has been a topic of active research since the 80‟s, proposing solutions to several practical

problems. Face recognition is probably the biometric method easier to understand, because we identify people by mainly

their faces. However the recognition process used by the human brain for identifying faces has not a concrete explanation.

Because it is now essential to have a reliable security systems in offices, banks, businesses, shops, etc. several

approaches have been developed, among them the face-based identity recognition or verification systems are a good

alternative for the development of such security systems.

In this paper we are going to compare the various histogram equilization algorithms They are:1)histogram

equalization 2) adaptive histogram equalization, 3)contrast limiting adaptive histogram equalization or CLAHE,4)local

histogram equilization and our proposed algorithms is 5) Oreinted local histogram equilization.

II. ALGORITHMS

On this section we present the different techniques used on the system proposed.

A) Histogram equalization:

The histogram manipulation, which automatically minimizes the contrast in areas too light or too dark of an

image, consists of a nonlinear transformation that it considers the accumulative distribution of the original image; to

generate a resulting image whose histogram is approximately uniform. On the ideal case, the contrast of an image would

be optimized if all the 256 intensity levels were equally used. Obviously this is not possible due to the discrete nature of

digital data of the image. However, an approximation can be achieved by dispersing peaks in the histogram of the image,

leaving intact the lower parts. This process is achieved through a transformation function that has a high inclination

where the original histogram has a peak and a low inclination in the rest of the histogram.

Histogram Normalization is one of the most commonly used methods for preprocessing. In image processing,

the idea of equalizing a histogram is to stretch and redistribute the original histogram using the entire range of discrete

levels of the image, in a way that an enhancement of image contrast is achieved. The most commonly used histogram

normalization technique is histogram equalization where one attempts to change the image histogram into a histogram

that is constant for all brightness values. This would correspond to a brightness distribution where all values are equally

probable. For image I(x,y) with discrete k gray values histogram is defined by i.e. the probability of occurrence of the

gray level i is given by:

Where i ∈ 0, 1…k −1 grey level and N is total number of pixels in the image. Transformation to a new intensity

value is defined by:

Output values are from domain of [0, 1].To obtain pixel values in to original domain, it must be rescaled by the

K−1 value. Fig.3 shows the histogram equalization.

The widespread histogram equalization cannot correctly improve all parts of the image. When the original

image is irregularly illuminated, some details on resulting image will remain too bright or too dark. These are most

commonly used techniques of histogram adjustment. HE is to create an image with uniform distribution over the whole

brightness scale and HS is to make the histogram of the input image have a predefined shape.

Fig 3. : An original image, its histogram, Linear histogram equalization from left to right.

B) Adaptive histogram equalization:

In histogram equalization, the goal is to obtain a uniform histogram for the output image, so that an “optimal”

overall contrast is perceived. However, the feature of interest in an image might need enhancement locally. Adaptive

Histogram Equalization (AHE) computes the histogram of a local window centered at a given pixel to determine the

mapping for that pixel, which provides a local contrast enhancement. However, the enhancement is so strong that two

major problems can arise: noise amplification in “flat” regions of the image and “ring” artifacts at strong edges.

The neighboring tiles are then combined using bilinear interpolation to eliminate artificially induced boundaries.

The contrast, especially in homogeneous areas, can be limited to avoid amplifying any noise that might be present in the

image approach we have taken the DCT coefficients in zigzag pattern as shown in Fig. 4.


December- 2016, pp. 106-114


The DCT is performed on the entire image obtained after processing the input face images by adaptive

histogram equalization and logarithm transform.

Fig 4. Block feature of DCT coefficients and their selection in zigzag pattern.

After applying adaptive histogram equalization, we employed logarithm transform [10] for further enhancement

of the image.

i) Discrete cosine transform:

The DCT is a popular technique in imaging and video compression, which transforms signals from the spatial

representation into a frequency representation. The forward 2D-DCT [6, 10, 16] of a M × N block image is defined as.

),()()(),( 1

0

1

0 yxfvuvuC N

y

M

x

N

vy

M

ux

2

)12(cos

2

)12(cos (2)

The inverse transform is defined as

),()()(

0

1

0

1),( vuCvu

v

N

u

Myxf

N

vy

M

ux

2

)12(cos

2

)12(cos (3)

where

x and y are spatial coordinates in the image block, u and v are coordinates in the DCT coefficients block. Fig.5 shows the

properties of the DCT coefficients in M × N blocks with the zigzag pattern used by JPEG [10] compression to process the

DCT coefficients. Although the total energy remains the same in the M × N blocks, the energy distribution changes with

most energy being compacted to the low-frequency coefficients. In our approach we have taken the DCT coefficients in

zigzag pattern as shown in Fig. 4.

The DCT is performed on the entire image obtained after processing the input face images by adaptive

histogram equalization and logarithm transform.

(a) (b) (c)

Figure 5. (a) Original Image with its histogram; (b) After histogram equalization; (c) After adaptive histogram

equalization.


December- 2016, pp. 106-114


C) Contrast Limiting Adaptive Histogram Equalization or Clahe:

The variants of Histogram Equilization (HE) Adaptive Histogram Equalization (AHE) and Contrast Limited

Histogram Equalization (CLHE).

AHE method divides the image into small contextual regions called „tiles‟ and HE is applied to all the regions.

The tiles are then stitched back using bilinear interpolation method. The AHE method increases the local contrast of the

image but it results in noise amplification. To limit the noise amplification, CLHE method clips the height of the

histogram based on threshold. Pisano et al. proposed CLAHE method for enhancing the low-contrast medical images

[16]. The Contrast Limited Adaptive Histogram (CLAHE) is a combination of AHE and CLHE. CLAHE divides the

image into small contextual regions called „tiles‟ and does histogram clipping based on threshold. The CLAHE method

enhances the local details of the image.

In Contrast Limited Histogram Equalization (CLHE), the histogram is cut at some threshold and then

equalization is applied. It improves the local contrast of image. In CLHE, since the peak of the histogram in each tile is

clipped, the amount of over amplification is avoided.

Contrast Limited Adaptive Histogram Equalization (CLAHE) [27] method uses the concepts of AHE and CLHE.

It uses small contextual regions called „tiles‟ and the peaks of histogram are clipped. It increases the local contrast of the

image and the noise amplification is avoided. The disadvantage of CLAHE method is it gives unsatisfactory results when

there is unbalanced contrast and increased brightness [54]. Thus, here Enhanced CLAHE method is proposed by

combining CLAHE with threshold technique.

i) Thresholding:

Thresholding is the one of the simplest, computationally faster method which is used in image processing for

image segmentation . Given image „f‟, the object can be extracted from its background with threshold „T‟ and create

image „g‟ using the Eq.(4).

The above method is called as global threshold. However if image has noise or illumination, global threshold

would not give good results and variable thresholding has to be applied.

Steps of CLAHE:

1) First, create an illumination invariant face image by combining CLAHE and thresholding. The image obtained is

termed as Enhanced Contrast Limited Adaptive Histogram Equalization (Enhanced CLAHE).

2) Second, the performance of the CLAHE and Enhanced CLAHE methods are compared. The face recognition of

the two methods is tested using the three public databases AR, Yale and ORL. The train database and test

database are created for testing the two methods. The images in the test database are matched with images in

train database using Fuzzy K Nearest Neighbour classifier. The face recognition accuracy rate percentage is the

number of correct matches in test database divided by the total number of images in test database. The face

recognition accuracy rate percentage of CLAHE and the proposed method „Enhanced CLAHE‟ are computed

for three public face databases AR, Yale and ORL.

3) The efficiency of the proposed method is compared with CLAHE by using the subspace projection method. The

subspace projection method „fisher face‟ is used here to extract the features from CLAHE and Enhanced

CLAHE images. The feature extraction step is done in both train and test database images. The features in test

database images are matched with features of train database image using cosine similarity measure. The face

recognition accuracy rate percentage, Equal Error Rate (EER), Half Total Error, False Acceptance Rate (FAR)

at 1%, 0.1% and 0.01% are calculated for both CLAHE and Enhanced CLAHE methods.

The results for Enhanced CLAHE in AR face database image are shown here. The results of pre-processing

steps as discussed in section 3.4.1 are shown in Fig.6(a)-Fig.6(d).

Fig.6. a) Colour image b) Gray c) Average filter d) Contrast Limited Adaptive histogram Equalization (CLAHE).

D) Local Histogram Equalization:

An extension to HE known as Local Histogram Equalization (LHE) has been introduced. The main idea of LHE

is to define a local transform function for each pixel based on its surrounding neighbouring pixels.


December- 2016, pp. 106-114


Fig 7. The basic concept of LHE operation

Generally, LHE uses a small window to define a Contextual Region (CR) for the center pixel of that window.

The relationship between the image and the window is illustrated in Fig. 9. Only the block of pixels that fall in this

window is taken into account for the calculation of CDF. Thus, as the window slides, the CDF is modified.

The CDF is the main contributor for the HE transform function. Hence, in LHE, the transform function of a

pixel is depending on the statistics of its neighbors in CR. Because the transform function changes as a response to the

changes in the contents of CR, LHE is also popularly known as Adaptive Histogram Equalization (AHE). Examples of

LHE methods are Non-Overlapped Block HE (NOBHE) , Block Overlapped HE (BOHE) and , Interpolated Adaptive

HE (IAHE) , Weighted Adaptive HE (WAHE) , Contrast Limited Adaptive HE (CLAHE) , Variable Region Adaptive

HE(VRAHE) , Local Information HE (LIHE) , Spatio- Temporally Adaptive HE (STAHE) , Partially Overlapped Sub-

Block HE (POSHE) , Conditional Sub-Block Bi-HE (CSBHE) [39], and Multiple Layers Block Overlapped HE

(MLBOHE) .

Since illumination generally contributes low-frequency component of images, and edges contributes high-

frequency component of images, some works tried to compensate the illuminated low-frequency image component. We

define the generalized LHE operator as:

(5)

where ξ, η is the relative position of the anchor point to the pixel to be processed, HWI is the input image whose

dimension is W-by-H, and HWI is the histogram-equalized image with the same dimension. The typical LHE which

uses the k-by-k local window can be denoted as 0,0

kL , since the anchor point is exactly the pixel to be processed itself. If

the pixel to be processed is brighter than all the neighbouring pixels around it, it will have a large intensity value after the

local histogram equalization, and vice versa. We can make LHE „oriented‟ by changing anchor positions. Fig. 10 shows

nine LHE operators using 3-by-3 windows.

Fig 7. Illustration of LHE

(a)


December- 2016, pp. 106-114


(b)

Fig 8. (a) Nine generalized LHE operators using a 3-by-3 window. (b) Illustration of anchors (blue circles) and the

corresponding pixels to be equalized (red „X‟s) on local search windows.

Comparison of the facial image in Fig. 9 after applying aO3 and

0,0

3L Note that the edges are enhanced using the

proposed aO3 .

E) Oriented Local Histogram Equalization:

In this section, we introduce a novel method of facial image preprocessing algorithm called Oriented Local

Histogram Equalization (OLHE). OLHE is similar to local histogram equalization (LHE), but it captures the orientation

of edges while LHE does not.We begin with a brief review on LHE. For each pixel on an image, we perform the

histogram equalization on the local w-by-h window centering on the pixel using.

(5)

where x is the pixel intensity value, cd f (x) is the cumulative distribution function of the histogram of the pixel intensities

in the w-by-h window, cd fmin is the minimum intensity in this window, and L is the desired number of output gray levels.

Typically a square window is used, and we define k ≡ w = h. We call the center of the k-by-k window the anchor. For

LHE, the anchor point is the pixel to be processed itself.

The eight operators with {ξ, η} other than {0, 0} are „oriented‟, and they are dubbed as the Oriented Local

Histogram Equalization operators (OLHE operators) in this paper. The following gives the formal definition of the

OLHE operators:

where k is an odd number. Note that according to our definition, there will always be exactly eight OLHE operators no

matter what the value of k is. Given an image I , OLHE produces 8 images, The images are referred to as the OLHE

images. We apply the OLHE operators to some basic image elements, such as edges and gradients, and Fig 10. shows

the results. In Fig. 3, the first row shows the input images, and the second row shows the corresponding results after

applying OLHE operators or the LHE. Some conclusions can be drawn from Fig. 10. 1) For ideal edges [Fig. 10 (b) and

(c)], LHE operator and all OLHE operators have exactly the same response. 2) For color gradients, LHE operator yields

uniform responses regardless of the directions of color gradients.

The OLHE operators, on the other hand, capture the directions of the color gradients. For example, O3 yields

the strongest response when it is applied to the image with a strong diagonal gradient that gradually intensities from

upper-left to lower-right, as what is shown in Fig. 10 (e). It can be also seen that operators

3O and

3O yield the second

strong responses, since the diagonal color gradient has components in both horizontal and vertical directions. 3) For a


December- 2016, pp. 106-114


pixel, if all the intensities of all the pixels within a local window centered at its upper-left location are smaller than the

intensity of this pixel, O_ 3 will have the strongest response. It is hence O_ 3 captures the diagonal gradient that

intensifies from upper-left to lower-right, or diagonal edges span from upper-right to lower-left.

Likewise, the rest of the seven OLHE operators capture gradients and edges of different 8 directions.

Fig. 11 shows facial images under different illumination conditions after applying the eight OLHE operators. Note that

different operators emphasize edges and gradients of different directions.

i) Notes on OLHE Implementation:

To obtain the OLHE results, given an image, we need to apply 8 operators to obtain 8 OLHE images, and it

seems 8 times of LHE-like operations on the whole image are required at the first glance. However, since the histogram

equalization result of each local window actually provides the results of OLHE operators at different pixel locations, we

actually need only one LHE-like operation on the whole image.

Fig 12: Implementing OLHE efficiently.

Each histogram-equalized local window can update different OLHE images at different pixel locations. For

example, in Fig. 6 (a), assume there is a 5*5 local patch on the image, and the coordinate of the upper-left-most and the

lower-right-most pixel is {1, 1} and {5, 5}, respectively. After performing the local histogram equalization within the

3*3 window centering at {3, 3}, the results at {2, 2} is exactly O 3 {2, 2}, and the results of the rest operators can be

similarly obtained, as shown in this figure. It is thus performing the local histogram equalization on every pixel once

yields the 8 OLHE images. Note that the histogram equalization results of the 8 surrounding pixels are ignored in

traditional LHE method. It is thus OLHE encodes more information than LHE. Assume a W-by-H image is given and the

local window of size k-by-k is applied, it requires W · H · k2 histogram bin updates to compute the 8 OLHE images.

ii) Face Recognition Using OLHE:

A straightforward way to apply the OLHE to face recognition is to concatenate all the eight resulting image,

forming an image of size 8∗W ∗ H, given the input image of size W ∗ H.

We define such a new image as

(6)

where the superscript „c‟ stands for „cascaded.‟ The new image obtained using c

kO contains profound information on

edges, gradients and their orientations. As will be seen in our experimental results, yielded promising overall face

identification rates.

When lower feature dimension is desired, one can also combine the OLHE images to form the image that has

the same dimension with the input image. Several ways to reduce the dimension are possible. Maybe the most

straightforward way is to sum them up with equal weights. We define


December- 2016, pp. 106-114


(7)

where the superscript „a‟ stands for „average‟ where and the ∑ is the

matrix summation. This representation is more compact than )(IOc

k meanwhile, by summing up the edges of 8

orientations a

kO has very good edge-preserving capability.

Fig 12. (a) and (c) Two input images with limited variance in pixel intensities since many local regions. (b) and (d)

Corresponding output of OLHE.

III. EXPERIMENTAL RESULTS We evaluated the proposed algorithm on 3 public available data sets: AR , CMU PIE , and Extended Yale B . The

experiments on AR studied face recognition via OLHE using different feature combination schemes with different

window sizes. These experiments also compared the performance of OLHE to those of other algorithms. The

experiments on CMU PIE, and Extended YaleB reported the performance of OLHE under the standard protocols. These

experiments provided a direct comparison between OLHE and the state-of-the-art algorithms.

As this paper focused on illumination compensation, only frontal faces without variations in head pose or facial

expression were considered. All the images were cropped according to the eye centers using the ground truth, and resized

to 80×100 pixels. In all the experiments except TABLE V, only the uniformly frontally lit images were used as

enrollment in the gallery. Images with variations in illumination conditions were used as probes to be identified. To

perform the face identification, we used the Nearest-Neighbor classifier on the processed intensities.

A. Performance Evaluation Under AR Dataset

AR data set [28] included facial images from 126 individuals. Each person had two sets of frontal images shot at

different time. Each set contained 13 images, one was uniformly-lit face without facial expressions, three were shot with

facial expressions, three were shot under three different lighting conditions, and the rest six were shot with sun glasses or

scarfs. We used faces without facial expressions, sun glasses and scarfs. The uniformly-lit faces were used as enrollment

in the gallery, and the faces with lighting variations were used as probes to be identified.

Fig 13. Results of the identification accuracy of LHE and OLHE on AR dataset using different kernel size. Fig. 13 shows

the face identification results using LHE and OLHE with different feature combination schemes c

kO and a

kO as well as

OLHE with Low-Intensity-Variation Pixel masking scheme (i.e. m

kO )

B. Comparison Between OLHE and State-of-the-Art Algorithms on PIE and Extended Yale B Dataset:

CMU PIE data set contained 41368 images of 68 individuals. Each person was imaged under 13 different poses,

43 different lighting conditions and 4 different facial expressions. We used the frontal faces under 21 different lighting


December- 2016, pp. 106-114


conditions with background lighting off.We randomly selected one single face for each person for enrolment, and the rest

20 faces were used for recognition. TABLE I gives the error rates of different algorithms in different data sets.

Table 1 : Different algorithm error rates

ALGORITHMS DATASETS(ERROR %)

AR

DATASET

CMU

PIE

EXTENDED

YALE B

HISTOGRAM EQUILIZATION 48.5 47.8 48.5

ADAPTIVE HISTOGRAM

EQUILIZATION

20.1

23.2 64.1

CONTRAST LIMITED

HISTOGRAM EQUILIZATION

17.8 0.36

78.0

LOCAL HISTOGRAM

EQUILIZATION

5.1

69.3 4.89

ORIENTED LOCAL

HISTOGRAM EQUILIZATION

4.1 0.1 1.0

IV. CONCLUSION

Histogram equalization is powerful method for image enhancement and it will increase the contrast of image.

The enhanced image will give the full dynamic range of histogram. However, histogram equalization process tries to

merge the adjacent gray levels together in order to force the uniformity of number of pixels in each appeared gray levels.

Consequently, the intensity saturation will be presented in darkness regions and whiteness region. Histogram equalization

assigns the intensity values of pixels in the input image such that the output image contains a uniform distribution of

intensities. It improves contrast and obtain a uniform histogram. This technique can be used on a whole image or just on

a part of an image..

We propose the Orientated Local Histogram Equalization (OLHE) that compensates illumination while

exploiting the edge orientations. We argue that the edge orientation is useful for face recognition. Three OLHE feature

combination schemes were proposed for face recognition: one encoded most edge orientations, one was more compact

with good edge-preserving capability, the other performed exceptionally well when extreme lighting conditions occur.

We also showed that LBP is a special case of OLHE, and OLHE is more effective than LBP for face recognition. The

computational complexity required by OLHE is relatively low compared with state-of-the-art algorithms such as LTP

that involves an additional chain of preprocessing or TVQI that requires solving a variational problem. We evaluated

OLHE on AR, CMU PIE, and Extended Yale B data sets, and showed the proposed algorithm outperformed the state-of-

the art algorithms in most cases.

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[4] H.Wang and N. Ahuja, “Facial expression decomposition,” in Proc.IEEE Int. Conf. Computer Vision, 2003,

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[5] Karel Zuiderveld, “Contrast Limited Adaptive Histogram Equalization”, Graphics Gems IV, 1994. A.S.

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Volume 6, Issue 12, December 2016 ISSN: 2277 128X …ijarcsse.com/.../Volume_6/12_December2016/V6I12-0149.pdf · 2017. 1. 3. · ( , ) u v C u v v N u M f x y D D » ¼ º «¬ ª