Principle Component Analysis (PCA) is an appearance-based technique for extraction of feature extraction that is commonly used in computer vision and image processing. This technique suffers from illumination variations, thus knowing which illumination control method to be used in PCA-based face recognition system is very important. This paper applies three selected normalization techniques; Discrete Cosine Transform (DCT), Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) to normalize face images. PCA was further used to extract features from the normalized face images. Euclidean distance was used to classify extracted features. The best recognition accuracy of 91.84% was obtained in DCT for ORL Database, while the best accuracy of 76% was achieved in DCT for FERET Database. The highest FAR of 0.9 was achieved in DCT for ORL Database, while the highest FAR of 0.5 was obtained in DCT and AHE for FERET Database. The highest FRR of 0.2821 was achieved in CLAHE for ORL Database, while 0.3000 was obtained in AHE for FERET Database. It was concluded that illumination control approaches have predominant effect on PCA–based facial recognition system. Keywords— Adaptive Histogram Equalization, Contrast Adaptive Histogram Equalization, Discrete Cosine Transform Illumination Normalization, Principal Component Analysi

Abikoye, Oluwakemi C

Aro, Taye O

Shoyemi, Iyabo F

FUOYE Journal of Engineering and Technology (FUOYEJET - Federal University Oye-Ekiti)

FUOYE Journal of Engineering and Technology, Volume 4, Issue 1, March 2019                            ISSN: 2579-0625 (Online), 2579-0617 (Paper) 
              
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Comparative Analysis of Illumination Normalizations on Principal Component 
Analysis Based Feature Extraction for Face Recognition 
Oluwakemi C. Abikoye, Iyabo F. Shoyemi and *Taye O. Aro 
Department of Computer Science, University of Ilorin, Ilorin, Nigeria 
kemi_adeoye@yahoo.com|{funmi2010 |taiwo774}@gmail.com 
 
Abstract— Principle Component Analysis (PCA) is an appearance-based technique for extraction of feature extraction that is commonly 
used in computer vision and image processing. This technique suffers from illumination variations, thus knowing which illumination control 
method to be used in PCA-based face recognition system is very important. This paper applies three selected normalization techniques; 
Discrete Cosine Transform (DCT), Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) 
to normalize face images. PCA was further used to extract features from the normalized face images. Euclidean distance was used to 
classify extracted features. The best recognition accuracy of 91.84% was obtained in DCT for ORL Database, while the best accuracy of 
76% was achieved in DCT for FERET Database. The highest FAR of 0.9 was achieved in DCT for ORL Database, while the highest FAR of 
0.5 was obtained in DCT and AHE for FERET Database. The highest FRR of 0.2821 was achieved in CLAHE for ORL Database, while 
0.3000 was obtained in AHE for FERET Database. It was concluded that illumination control approaches have predominant effect on PCA–
based facial recognition system.   
Keywords— Adaptive Histogram Equalization, Contrast Adaptive Histogram Equalization, Discrete Cosine Transform Illumination 
Normalization, Principal Component Analysis,   
——————————      —————————— 
1 INTRODUCTION 
acial recognition, among so many biometric 
recognition systems is considered a user friendly and 
confidentially respectful biometric technique with 
high accuracy (Jafri & Arabnia, 2009) and low 
intrusiveness (Kashem, Akhter, Ahmed & Alam, 2011). It 
is a technique of biometric for verifying and identifying 
a person from digital image or video using dataset of 
images of face (Joshi & Deshpande, 2015). The 
performance of recognition of face depends on some 
major factors like variations in illumination, pose 
changes, facial exposure, variations in age and 
occlusions (Reza & Qi, 2016). It takes enormous effort to 
develop accurate and robust recognition over a broad 
range of conditions.  
Face recognition and verification accuracy deteriorated 
significantly when there are differences in pose and 
illumination between images used for enrollment and 
recognition (Romdhani, Ho, Vetter & Kriegman, 2006). 
In the midst of these factors, illumination conditions 
such as shadows, underexposure (too dark) and 
overexposure (too bright) attracted much attention in the 
last decade (Abate, Nappi, Riccio& Sabatino, 2007). 
Illumination remains the most significant factor affecting 
the image appearance; it often leads to diminished 
structures or in homogenous intensities of the image due 
to different object texture surface and the shadows cast 
from different light source directions.  
Normalization of illumination is an essential aspect that 
must be duly considered in image processing and 
computer vision (Anila & Devarajan, 2012). Due to the 
problem of illumination variability in recognition of face 
using Principal Component Analysis (PCA) method, the 
same image can appear out rightly different even when 
it is captured in fixed pose. The challenge of knowing 
which illumination control methods to be used in facial 
recognition system based on PCA algorithm is very 
important.  
 
* Corresponding Author 
 
This  paper applied the most three commonly used 
methods of  illumination control/normalization 
techniques; Discrete Cosine Transform, Adaptive 
Histogram Equalization and Contrast Limited Adaptive 
Histogram Equalization on Principal Component 
Analysis algorithm to conduct  comparative analysis  on 
which of the illumination control techniques perform 
better. 
2 RELATED WORK 
Several studies have been proposed for which different 
techniques are employed by researchers to carrying out 
illumination normalization. Salkar et al., (2017) 
presented facial recognition system using local feature 
description with diverse lighting conditions. Local 
directional number pattern (LDN) was applied to extract 
the features from face image. Six-bit binary code was 
created to produce LDN image. The face image was 
divided into numerous sections and for each section 
LDN was calculated and histogram for each section 
taken. The system was evaluated on three face image 
datasets; CMU Multi-PIE (Carnegie Mellon University 
collected Multi-Pose Illumination Expression Database), 
YALE face image database and Olivetti Research 
Laboratory Database (ORL). Experimental results 
showed 70 % of accuracy for recognition. 
Reza and Qi (2016) proposed a system which produced 
illumination-invariant features for images irrespective of 
their level of illumination. The work combined adaptive 
homomorphic filtering, simplified logarithmic fractal 
dimension and complete eight local directional patterns 
to produce the illumination invariant features. They 
determined from their experiments that the developed 
system performed better than nine state of the art 
techniques and thus results in a better face recognition 
accuracy. 
Dan-ali (2014) came up with a study by conducting 
comparative analysis on five methods for control of 
illumination. The study performed a comparative 
analysis on the system based on diverse distance 
measure metrics. The performance and execution times 
F 
FUOYE Journal of Engineering and Technology, Volume 3, Issue 1, March 2018                          ISSN: 2579-0625 (Online), 2579-0617 (Paper) 
 
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of these methods were recorded and computed in terms 
for efficiency and accuracy. The illumination normalized 
methods were Gamma Intensity Correction (GIC), 
Discrete Cosine Transform (DCT), Histogram 
Remapping using Normal Distribution (HRN), 
Histogram Remapping using Normal Distribution (HRL) 
and Anisotropic Smoothing Technique (AS). 
Experimental results achieved enhanced recognition 
accuracy, when the better preprocessing method was 
used on suitable database using the correct classifier. 
Kalaiselvi and Nithya (2013) presented an effective 
preprocessing chain that removes the effects of 
illumination variations while still keeping information 
required for recognition. The study combined the 
strengths of local texture based face representations, 
robust illumination normalization, distance transform 
based matching and kernel based feature extraction and 
multiple feature fusion. Their work was simulated using 
MATLAB and their experiments showed that their 
method outperforms several other  preprocessing 
techniques catering for illumination. 
3 RESEARCH METHODOLOGY 
The comparative analysis system considered three 
selected illumination normalization approaches; 
Contrast Limited Adaptive Histogram Equalization, 
Discrete Cosine Transform and Adaptive Histogram 
Equalization. This study acquired face images from two 
publicly available face image datasets; Olivetti Research 
Laboratory (ORL Database) and Face Recognition 
Technology (FERET Database). 100 face images were 
used from the two datasets. The second phase handled 
the image normalization, which is the major focus of the 
study, which involved the illumination normalization 
techniques. Three illumination normalization techniques 
aforementioned were applied to pre-process the face 
images from the two databases. Principal Component 
Analysis (PCA) was applied for extraction of facial 
features from the face images. The extracted feature 
subspace was passed into Euclidean distance measure 
for classification of face into matched or mismatched. 
Finally, comparative analysis was conducted to show the 
effect of illumination normalization methods on the PCA 
extracted facial feature. The process of comparative 
analysis is shown in Figure 1. 
 
3.1 PERFORMANCE EVALUATION OF THE COMPARATIVE 
ANALYSIS 
The measurement metrics used in this study are shown 
in the following equations: 
 
Recognition Accuracy (%)   
     
             
         (1)  
 
    
                          
                                 
  
  
     
                 (2) 
 
     
               
                                 
  
  
     
             (3) 
 
Where True Positive (TP) denotes the total number of 
authorized face images that are correctly recognized by 
the system, False Positive (FP) that is the total number of 
unauthorized face images wrongly recognized by the 
system, True Negative (TN) represents the total number 
of authorized images correctly unrecognized and False 
Negative (FN) is the total number of unauthorized 
images that are wrongly unrecognized. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig. 1: Block Diagram of the System 
 
4 RESULTS AND DISCUSSION 
4.1 RESULTS OF RECOGNITION ACCURACY (RA) 
The results of the recognition accuracy are given for the 
two datasets; Olivetti Research Database of images and 
FERET face image database. Results are represented in 
Table 1. 
Table 1. Recognition Accuracy 
 
From Table 1, the best recognition accuracy of 91.84% 
was obtained in DCT technique compared with the AHE 
technique with 79.59% and CLAHE technique with 
77.47%. Thus, the result showed that the DCT technique 
performed effectively well in term of recognition 
accuracy for ORL Database.  Also for FERET Database, 
the best recognition accuracy of 76% was obtained in 
DCT method compared with the AHE method with 74% 
Illumination 
Normalization 
Method 
Recognition Accuracy (%) 
ORL Database FERET 
Database 
DCT 91.84 76 
AHE 79.59 74 
CLAHE 77.47 68 
Sample of face images
Illumination Normalization 
Techniques
Contrast Limited Adapted 
Histogram Equalization (CLAHE)
Discrete Cosine Transform (DCT)
Adaptive Histogram Equalization 
(AHE)
Normalized DCT images
Geometric Normalization of Images
Normalized AHE images Normalized CLAHE images
PCA PCA PCA
Euclidean Distance 
Face Image 
Matched / 
Mismatched
Extracted PCA features 
(DCT)
Extracted PCA features 
(AHE)
Extracted PCA features 
(CLAHE)
FUOYE Journal of Engineering and Technology, Volume 3, Issue 1, March 2018                          ISSN: 2579-0625 (Online), 2579-0617 (Paper) 
 
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and CLAHE technique with 68%. Thus, the experimental 
results of the recognition accuracy showed that the DCT 
technique outperformed other techniques for the two 
image datasets in term of recognition accuracy. 
4.2 RESULTS OF FALSE ACCEPTANCE RATE AND FALSE 
REJECTION RATE 
The results for false rejection rate and false acceptance 
are shown in Table 2 for FERET and ORL face image 
database respectively.  
Table 2. False Acceptance and False Rejection Rates 
 
 
4.3 SUMMARY AND DISCUSSION OF RESULTS 
Tables 3, shows the comparative analysis of the various 
illumination normalization techniques of PCA-based 
extracted features. It was observed that the DCT gave 
the highest FAR of 0.9 for ORL database, while the 
highest FAR of 0.5 was achieved in DCT and AHE for 
FERET Database. The best FRR of 0.0769 was obtained in 
DCT technique for ORL database, while the best FRR of 
0.0750 was achieved in DCT technique for FERET 
database. The best recognition accuracy of 91.84 % was 
achieved in DCT image for ORL database, also 76 % was 
obtained in DCT image of FERET database. 
   
 
 
 
 
 
Table 3. Comparative Analysis 
 
 
 
 
 
5 CONCLUSION 
Illumination has been identified to be one of the major 
problems in facial recognition system relying on 
Principal Component Analysis for feature extraction. 
Several approaches of illumination normalization have 
been employed such as Discrete Cosine Transform, 
Contrast Limited Adaptive Histogram Equalization and 
Adaptive Histogram Equalization. All these techniques 
have not really considered comparatively the impact of 
illumination control techniques. This paper carried out a 
comparative analysis to investigate the influence of 
illumination normalization methods on PCA-based face 
recognition system. Experimental results showed the 
best recognition accuracy of 91.84 % in Discrete Cosine 
Transform for ORL and 76 % for FERET. The lowest 
False Rejection Rate (FRR) of 0.0769 was recorded in 
Discrete Cosine Transform for ORL database. Finally, the 
best recognition accuracy and FRR were obtained in 
DCT technique for the two face datasets, thus revealed 
that this technique performed efficiently in controlling 
and reducing the problem of illumination for PCA-based 
feature extraction method of face recognition. Future 
work should consider the effect of illumination 
normalization methods on feature-based techniques of 
facial recognition system. Also, the number of face image 
dataset should be further increased in order to validate 
experimental results. 
 
 
 
 
REFERENCES 
Abate, A. F., Nappi, M., Riccio, D., & Sabatino, G. (2007). 2D and 3D 
face recognition: A survey. Pattern Recognition Letters, 28(14), 
1885–1906. http://doi.org/10.1016/j.patrec.2006.12.018 
Anila, S., & Devarajan, N. (2012). Preprocessing Technique for Face 
Recognition. Global Journal of Computer Science and Technology 
Graphics & Vision, 12(11), 13–18. 
Dan-ali, A. M. (2014). What is the Right Illumination Normalization 
for Face Recognition ? International Journal of Advanced Research in 
Artificial Intelligence, 3(12), 24–28. 
Jafri, R., & Arabnia, H. R. (2009). A Survey of Face Recognition 
Techniques. Journal of Information Processing Systems, 5(2), 41–68. 
Joshi, A. G. & Deshpande, A. S. (2015). Review of Face Recognition 
Techniques. International Journal of Advanced Research in Computer 
Science and Software Engineeering, 5(1), 71–75. 
Kalaiselvi, P., Nithya, S., & P.Kalaiselvui. (2013). Face Recognition 
System under Varying Lighting Conditions. Iosr-Jce, 14(3), 79–88. 
Retrieved from http://www.iosrjournals.org/iosr-
jce/papers/Vol14-issue3/M01437988.pdf?id=7441. 
Kashem, M. A., Akhter, N., Ahmed, S., & Alam, M. (2011). Face 
Recognition System Based on Principal Component Analysis 
(PCA) with Back Propagation Neural Networks (BPNN). 
International Journal of Scientific & Engineering Research, 2(6), 1–10. 
Reza, M., & Qi, X. (2016). Face recognition under varying 
illuminations using logarithmic fractal dimension-based 
complete eight local directional patterns. Neurocomputing, 199, 
16–30. http://doi.org/10.1016/j.neucom.2016.01.094. 
Romdhani, S., Ho, J., Vetter, T., & Kriegman, D. J. (2006). Face 
Recognition Using 3-D Models: Pose and Illumination. 
Proceedings of the IEEE, 94(11), 1977–1999. 
http://doi.org/10.1109/JPROC.2006.886019. 
Salkar, S. R., Patankar, N. S., Chintamani, R. D., & Deshmu, Y. S. 
(2017). Illumination Invariant Face Recognition using Local 
Directional Number Pattern ( LDN ). International Journal of 
Engineering Development and Research, 5(2), 303–308.  
Illumination 
Normalization 
Technique 
ORL Database FERET Database 
FAR FRR FAR FRR 
DCT 0.9 0.0769 0.5 0.0780 
AHE 0.6 0.2564 0.5 0.3000 
CLAHE 0.7 0.2821 0.3 0.2000 
Illumination 
Normalization 
Technique 
ORL Database FERET Database 
FAR FRR RA (%) FAR FRR RA (%) 
DCT 0.9 0.0769 91.84 0.5 0.0750 76 
AHE 0.6 0.2564 79.59 0.5 0.3000 74 
CLAHE 0.7 0.2821 77.47 0.3 0.2000 68 


Comparative Analysis of Illumination Normalizations on Principal Component Analysis Based Feature Extraction for Face Recognition

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Comparative Analysis of Illumination Normalizations on Principal Component Analysis Based Feature Extraction for Face Recognition

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