Face Identification Based on Contrast Limited Adaptive HistogramEqualization (CLAHE).

Gibran Benitez-Garcia, Jesus Olivares-Mercado, Gualberto Aguilar-Torres,Gabriel Sanchez-Perez and Hector Perez-Meana

Mechanical and Electrical Engineering School of National PolytechnicInstitute of Mexico. Mexico, Mexico D.F.

Abstract— This paper proposes a face identification methodbased on Contrast Limited Adaptive Histogram Equaliza-tion (CLAHE) robust to facial expressions, occlusion andspecially to illumination changes. Based on Eigenphasesalgorithm for feature extraction, the Principal ComponentsAnalysis (PCA) and the Phase Spectrum was used as featureextraction stage, and Support Vector Machine (SVM) as aclassifier. The results were obtained using a database thatincludes face images of 120 subjects (60 males and 60females) with illumination changes, facial expressions andpartial occlusion. The proposed method provides results witha correct recognition up to 97%.

Keywords: Face Identification, CLAHE, Eigenphases and SVM.

1. IntroductionIn business and personal life today, security protection

systems are critical for many application domains: trans-action protection, access control, computer and networksecurity, and most important, personal and public safety.Since the tragic terrorist attacks of September 11, 2001,there has been a greater awareness of security threats andincreased acceptance of more intrusive security systems [1].

Biometrics systems are a solution for this problem, be-cause are automated methods of verifying or identifying theidentity of a person on the basis of some physiological orbehavior characteristic [1].

It is important to consider the difference among identi-fication and verification. Identification is when the systemoutput determines the identity of the person with the highestapproximation among a set of known persons (saved in thedatabase) and verification is when the system determines ifthe person is whom he/she claims to be.

The biometric identification and verification methods canbe divided in two categories: behavioral methods such assignatures, keyboard typing, and voice print; and physiolog-ical methods such as fingerprint, iris pattern, palm geometry,DNA, and facial features [2].

The general structure of biometric system basically con-sists of a capture stage, when the pattern (either physiolog-ical or behavioral) is captured, a feature extraction stage,when the pattern will be converted in a vector feature,and the classification stage when generate templates based

on vectors features, and the system compares and decideswhether the extracted features vector agrees or disagreeswith the estimated template, Fig. 1 shows this structure.

Fig. 1: General structure of biometric system.

In particular the face recognition has been a topic of activeresearch because the face is the most direct way to recognizepeople [3]. Additionally, the data acquisition of this methodconsists in taking a picture, this doing the face recognitionone of the biometric methods with larger acceptance amongthe users.

Over the past two decades, the problem of face recognitionhas attracted substantial attention from various disciplinesand has witnessed an impressive growth in basic and ap-plied research, product development, and applications. Facerecognition systems have already been deployed at ports ofentry at international airports such as Australia and Portugal[4].

In recent years, there have been proposed different facerecognition methods to improve the identification accuracy[1], [3], [4]. However, the variations in face images used insystems decreases the accuracy drastically. These variationsarise mainly from changes in facial expressions, as well asillumination conditions in which they are, and in some casespartial occlusion.

M. Savvides et al proposed the Eigenphases algorithm[5], which focused on feature extraction stage, reduces theillumination problems that affect the recognition of faces, asit uses the phase extracted from the Fast Fourier Transformtogether with Principal Components Analysis (PCA) toobtain the main features of that stage. A variation of thismethod is to include a pre-processing stage in which theface images is adapted, to insert an "enhanced image" inthe stage of feature extraction, histogram equalization [6]and the normalization of an image [7] are some methods toadjust the images on the pre-processing stage.

This paper proposes a face identification algorithm us-ing Contrast Limited Adaptive Histogram Equalization

(CLAHE) in the pre-processing stage to enhance the illumi-nation of the face images, the PCA and the Phase Spectrumare used in the features extraction stage, and the SupportVector Machine (SVM) as classifier. The results obtainedwith the proposed method are compared with Eigenphases[5] and Eigenphases using Histogram Equalization [6].

The proposed and conventional methods are evaluatedunder the same conditions, using a face database created inthe National Polytechnic Institute of Mexico which includes24 face images of 120 subjects with different illumination,facial expressions variations and partial occlusion.

2. Proposed SystemThe proposed algorithm for face identification is shown in

Fig. 2, the system output provides the identity of one personamong all, that are in the database.

Fig. 2: Proposed face identification algorithm.

The method is divided in two phases (training and iden-tification) and both consist of four modules: CLAHE: thismodule belongs to the pre-processing stage, this is where theimage is enhanced; Obtain Phase Spectrum: in this moduleobtains phase extracted from the Fast Fourier Transform;PCA: this and the previous module are in the stage of featureextraction; and SVM: this module gets the templates for thetraining phase and making the decision in the identificationphase.

2.1 A. Contrast Limited Adaptive HistogramEqualization

Firstly the histogram of a digital image with intensitylevels in the range [0, L− 1] is a discrete function:

h(rk) = nk (1)

Where rk is the kth intensity value and nk is the numberof pixel in the image with intensity rk [8]. A normalizedhistogram is given by:

pr(rk) =nk

MNk = 0, 1, 2, . . . , L− 1 (2)

Loosely speaking pr(rk) is an estimated of the probabilityof occurrence of intensity level rk in an image. The sum ofall components of normalized histogram is equal to 1.

The histogram equalization is a method in image pro-cessing of contrast adjustment using the image’s histogram.This method usually increases the global contrast of manyimages, through transforming the original image histogramto a uniform histogram, that is, trying to make uniformthe distribution intensity pixels of the image. The histogramequalization is obtained by next equation:

sk = (L− 1)k∑

j=0

pr(rj) k = 0, 1, 2, . . . , L− 1 (3)

where sk is the new distribution of the histogram.This procedure is based on the assumption that the image

quality is uniform over all areas and one unique grayscalemapping provides similar enhancement for all regions of theimage. However, when distributions of grayscales changefrom one region to another, this assumption is not valid. Inthis case, an adaptive histogram equalization technique cansignificantly outperform the standard approach. In this case,the image is divided into a limited number of regions and thesame histogram equalization technique is applied to pixelsin each region [9].

Even in some cases this method can not resolve theproblem, when grayscale distribution is highly localized, itmight not be desirable to transform very low-contrast imagesby full histogram equalization. In these cases, the mappingcurve may include segments with high slopes, meaning thattwo very close grayscales might be mapped to significantlydifferent grayscales. This issue is resolved by limiting thecontrast that is allowed through histogram equalization.The combination of this limited contrast approach with theaforementioned adaptive histogram equalization results inwhat is referred to as Contrast Limited Adaptive HistogramEqualization (CLAHE) proposed in [10]. The CLAHE pro-cedure consists in:

First the image has to be divided into several non-overlapping regions of almost equal sizes. Secondly thehistogram of each region is calculated. Then, based on adesired limit for contrast expansion, a clip limit for clippinghistograms is obtained. Next, each histogram is redistributedin such a way that its height does not go beyond the cliplimit. The clip limit β is obtained by:

β =MN

L

(1 +

α

100(smax − 1)

)(4)

where α is a clip factor, if clip factor is equal to zero theclip limit becomes exactly equal to (MN

L ), moreover if cliplimit is equal to 100 the maximum allowable slope is smax.

Finally, cumulative distribution functions (CDF) of theresultant contrast limited histograms are determined forgrayscale mapping. The pixels are mapped by linearly com-bining the results from the mappings of the four nearestregions; this process is explained in [11].

The Fig. 3 shows the differences among histogramsof same image, applying both methods before mentionedCLAHE and Histogram Equalization.

Fig. 3: Comparison of CLAHE and Histogram Equalization.a) Original image and its histogram. b) CLAHE imagedividing (a) in four regions of equal size and its histogram.c) Histogram equalization image and its histogram.

2.2 Phase SpectrumOppenheim et al. [12] show that phase information retains

the most part of the intelligibility of an image, because thephase spectrum contains most of the image information. Thiscan be computed trough of a Fourier Transform which isgiven by:

F (u) = |F (u) expjθ(u) | (5)

where the magnitude is:

|F (u)| = [R2(u) + I2(u)]12 (6)

and the phase is:

θ(u) = arctan[

I(u)R(u)

](7)

This is also demonstrated by Oppenheimt’s experimentshown in Fig. 4, in this experiment the Fourier Transformwas applied to these two images and obtain the magnitudeand phase. If combine the phase of the image 1 with themagnitude of the image 2 and the phase of the image 2 withthe magnitude of the image 1, is prove that the componentthat provides more information about the image is the phase.

Fig. 4: Oppenheimt’s experiment.

2.3 Principal Components AnalysisThe PCA is a way of identifying patterns in data, and

expressing the data in such a way as to highlight theirsimilarities and differences. Since patterns in data can behard to find in data of high dimension, where the luxury ofgraphical representation is not available, PCA is a powerfultool for analyzing data [13].

The other main advantage of PCA is that once thesepatterns are found in the data, and the data is compress,the number of dimensions are reduce, without much loss ofinformation.

Fig. 5: Feature extraction system by PCA.

Fig. 5 shows the procedure for PCA application whichis used in both phases training and recognition. The nextprocedure was used in training phase:

Firstly the training images are converted in column vec-tors, and then these vectors make a matrix (Principal Ma-trix). Next the principal components were extracted of thePrincipal Matrix to obtain a matrix of dominant features(D.F). Finally the vector of each person is multiplied byD.F to generate a feature vector, subsequently these vectorsconform a matrix of feature vectors. This step is only usedin the identification phase..

2.4 Support Vector MachineA support vector machine is basically a binary pattern

classification method, whose objective is to assign eachpattern to a class [14].

The SVM is used differently in each one of the phasesusing the vectors features obtained by PCA method. Ontraining phase the SVM generates templates of each person,and in recognition phase decides whether feature vectoragree or disagree with all templates.

Fig. 6: Decision step by SVM in identification phase.

The decision step is shows in Fig. 6. The feature vectorof the person to recognize is applied to all one vs. allSVMs. The class given the Maximum Likelihood is used asthe person’s identity; the equation to obtain the MaximumLikelihood is as follows:

S = arg max1≤k≤S

P (λk|x) (8)

where S is the winner and thus revealing the person’sidentity to whom this image was assigned, x is the columnvector of the image to analyze and λk is the SVM model ofthe person k [15].

2.5 Algorithm VariationsAs mentioned earlier the proposed system was compared

with the original method of Eigenphases [5] and the based inHistogram Equalization [6], for this comparison there wereperformed three variations in the proposed algorithm. It isnoteworthy that variations are on the pre-processing stage

in conjunction with the obtain phase spectrum, the result isconvert in vector for initialize the step of PCA.

The first modification (CLAHE (2,2)) consists in applythe CLAHE dividing the original image in 2 parts in y axisand 2 in x axis generating four blocks, for later apply theFast Fourier Transform (FFT) to obtain the phase spectrumof full image, this process shown in Fig. 7.

Fig. 7: First variant of system called CLAHE (2,2).

This variation is compare in the evaluation results with onemethod proposed in [6] which used histogram Equalizationin full image for after extract the phase spectrum also of fullimage (Full HE). Moreover is compared with the originalEigenphases method. The Fig. 8 shows the process of thesetwo methods.

Fig. 8: Differences among CLAHE (2, 2) and Full HE (whitelines are not part of the image, only used to illustrate thenumber of divisions used by CLAHE).

The second variation (CLAHE (8,6)) is based on applyingthe CLAHE and dividing the original image in 8, 6 parts (y,x axes) generating 48 blocks each one of 6x6 pixels, nextthe Fast Fourier Transform is applied to extract the phasespectrum of full image, the Fig. 9 shows this process.

Fig. 9: Second variant of system called CLAHE (8,6).

This method which compares the face image is firstlysegmented in blocks of size 6x6. Next the histogram equal-ization is applied to each block, which concatenated toreconstruct the face image under analysis. Finally the FourierTransform is applied to the whole image to estimate thephase spectrum (Local HE), as shown in Fig. 10.

Fig. 10: Differences among CLAHE (8,6) and Local HE(white lines are not part of the image, only used to illustratethe 48 blocks used by CLAHE).

The Fig. 11 shows the finally variation of the system,which consists on applying the CLAHE to form 48 blocksof 6x6 pixels as in the previous structure, next the FastFourier Transform is applied to each block, to estimate thephase spectrum of the face image, finally these blocks areconcatenated to reconstruct the phase spectrum of the faceimage, which is obtained using the estimated phase of eachblock (Fourier CLAHE).

Fig. 11: Final variant of system called Fourier CLAHE.

The final method is compared with its similar with HEthis is shown in the Fig. 12, that is the same procedure asLocal HE but the Fast Fourier Transform is applied to eachblock, to estimate the phase spectrum of the face image, andwith these blocks which are concatenated to reconstruct thephase spectrum of the face image, which is obtained usingthe estimated phase of each block called Fourier HE.

3. EVALUATION RESULTSTo evaluate the results of proposed and conventional

methods, there have realized the tests under the same

Fig. 12: Differences among Fourier CLAHE and FourierHE (white lines are not part of the image, only used toillustrate the concatenation of the blocks by phase spectrumextracted).

conditions, using a face data base created in to the NationalPolytechnic Institute of Mexico which contains 2880face images. This data base includes 24 face images of120 subjects, 60 males and 60 females, under controlledconditions such as different illumination, facial expressionsvariations and partial occlusion using sunglasses, the sizeof the images is 480 x 360 pixels. As shown in Fig. 13.

Fig. 13: Example of the face images in the database.

Six images were used for the training phase to generate atemplate for each person, all images are resized to 48 x 36pixels, to calculate the PCA. The number of training imageswas calculated trying to get the best results with the leastnumber of images possible. The results for obtaining thenumber of training images are shown in the graph on Fig.14.

Fig. 14: Graph of results to determine the number of trainingimages.

The graph shown the result for the recognition using 2,3, 5 and 6 images in training phase applying "Full HE"variation of the system, this test was performed with alldatabase using the 24 face images of 120 people. Thereforein the following results there were used 6 images for training,these images shown in Fig. 15.

Fig. 15: Training images example.

In the variations using HE and the original algorithmEigenphases the results is shown in Table 1, in which itis noted that the best result is obtained by Fourier HE en-hancement about 6 percentage points to the original method.

Table 1: RESULTS WITH HE VARIATIONS AND EIGEN-PHASE METHOD

Result %Eigenphases 91.22

Full HE 91.01Local HE 94.48

Fourier HE 97.19

The Table 2 shows the results of CLAHE method and itsvariations. All these results are better than the obtained usingthe original method, Fourier CLAHE was the best of theseresults.

Table 2: RESULTS WITH CLAHE VARIATIONSResult %

CLAHE (2,2) 91.53CLAHE (8,6) 91.35

Fourier CLAHE 97.36

The Fig. 16 shows the comparison among the results ofEigenphases with the best result of HE variation (FourierHE) and the best of CLAHE variation (Fourier CLAHE).Fourier CLAHE is the best result of this comparison, pro-viding accuracy identification from 97.36%.

Fig. 16: Comparison among “Fourier HE ”and “FourierCLAHE”with the conventional method “Eigenphases”, usingthe same conditions.

4. ConclusionThe best identification result obtained in this paper is the

"Fourier CLAHET, surpassing 6% to conventional methodEigenphases [5] and a little " Fourier HE ", as seen in Figure16. "Fourier HE" in turn is the best result obtained usingHistogram Equalization variations proposed in [6].

It is important to mention that the 3 proposed variationsusing CLAHE improve the conventional method Eigen-phases, in contrast to the 3 variations using HE as it "FullHE" presents a lower assertiveness than Eigenphases.

Moreover, the proposed system shown to be robust tochanges in the database used, which are illumination changesand partial occlusion by using sunglasses, where the resultsobtained are greater than 90% and in the best cases obtaineda 97.36% which is acceptable for a face recognition system.

AcknowledgmentThanks to everyone who helped make this project, espe-

cially those who gave their time voluntarily for the realiza-tion of the database.

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