Face Identification Based on Contrast Limited Adaptive Histogram Equalization (CLAHE).

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1 Face Identification Based on Contrast Limited Adaptive Histogram Equalization (CLAHE). Gibran Benitez-Garcia, Jesus Olivares-Mercado, Gualberto Aguilar-Torres, Gabriel Sanchez-Perez and Hector Perez-Meana Mechanical and Electrical Engineering School of National Polytechnic Institute of Mexico. Mexico, Mexico D.F. Abstract This paper proposes a face identification method based on Contrast Limited Adaptive Histogram Equalization (CLAHE) robust to facial expressions, occlusion and specially to illumination changes. Based on Eigenphases algorithm for feature extraction, the Principal Components Analysis (PCA) and the Phase Spectrum was used as feature extraction stage, and Support Vector Machine (SVM) as a classifier. The results were obtained using a database that includes face images of 120 subjects (60 males and 60 females) with illumination changes, facial expressions and partial occlusion. The proposed method provides results with a correct recognition up to 97%. Keywords: Face Identification, CLAHE, Eigenphases and SVM. 1. Introduction In business and personal life today, security protection systems are critical for many application domains: transaction protection, access control, computer and network security, 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 and increased acceptance of more intrusive security systems [1]. Biometrics systems are a solution for this problem, because are automated methods of verifying or identifying the identity of a person on the basis of some physiological or behavior characteristic [1]. It is important to consider the difference among identification and verification. Identification is when the system output determines the identity of the person with the highest approximation among a set of known persons (saved in the database) and verification is when the system determines if the person is whom he/she claims to be. The biometric identification and verification methods can be divided in two categories: behavioral methods such as signatures, keyboard typing, and voice print; and physiological methods such as fingerprint, iris pattern, palm geometry, DNA, and facial features [2]. The general structure of biometric system basically consists of a capture stage, when the pattern (either physiological 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 decides whether the extracted features vector agrees or disagrees with 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 active research because the face is the most direct way to recognize people [3]. Additionally, the data acquisition of this method consists in taking a picture, this doing the face recognition one of the biometric methods with larger acceptance among the users. Over the past two decades, the problem of face recognition has attracted substantial attention from various disciplines and has witnessed an impressive growth in basic and applied research, product development, and applications. Face recognition systems have already been deployed at ports of entry at international airports such as Australia and Portugal [4]. In recent years, there have been proposed different face recognition methods to improve the identification accuracy [1], [3], [4]. However, the variations in face images used in systems decreases the accuracy drastically. These variations arise mainly from changes in facial expressions, as well as illumination conditions in which they are, and in some cases partial occlusion. M. Savvides et al proposed the Eigenphases algorithm [5], which focused on feature extraction stage, reduces the illumination problems that affect the recognition of faces, as it uses the phase extracted from the Fast Fourier Transform together with Principal Components Analysis (PCA) to obtain the main features of that stage. A variation of this method is to include a pre-processing stage in which the face images is adapted, to insert an "enhanced image" in the stage of feature extraction, histogram equalization [6] and the normalization of an image [7] are some methods to adjust the images on the pre-processing stage. This paper proposes a face identification algorithm using Contrast Limited Adaptive Histogram Equalization

2 (CLAHE) in the pre-processing stage to enhance the illumination of the face images, the PCA and the Phase Spectrum are used in the features extraction stage, and the Support Vector Machine (SVM) as classifier. The results obtained with the proposed method are compared with Eigenphases [5] and Eigenphases using Histogram Equalization [6]. The proposed and conventional methods are evaluated under the same conditions, using a face database created in the National Polytechnic Institute of Mexico which includes 24 face images of 120 subjects with different illumination, facial expressions variations and partial occlusion. 2. Proposed System The proposed algorithm for face identification is shown in Fig. 2, the system output provides the identity of one person among all, that are in the database. Fig. 2: Proposed face identification algorithm. The method is divided in two phases (training and identification) and both consist of four modules: CLAHE: this module belongs to the pre-processing stage, this is where the image is enhanced; Obtain Phase Spectrum: in this module obtains phase extracted from the Fast Fourier Transform; PCA: this and the previous module are in the stage of feature extraction; and SVM: this module gets the templates for the training phase and making the decision in the identification phase. 2.1 A. Contrast Limited Adaptive Histogram Equalization Firstly the histogram of a digital image with intensity levels in the range [0, L 1] is a discrete function: h(r k ) = n k (1) Where r k is the kth intensity value and n k is the number of pixel in the image with intensity r k [8]. A normalized histogram is given by: p r (r k ) = n k MN k = 0, 1, 2,..., L 1 (2) Loosely speaking p r (r k ) is an estimated of the probability of occurrence of intensity level r k in an image. The sum of all components of normalized histogram is equal to 1. The histogram equalization is a method in image processing of contrast adjustment using the image s histogram. This method usually increases the global contrast of many images, through transforming the original image histogram to a uniform histogram, that is, trying to make uniform the distribution intensity pixels of the image. The histogram equalization is obtained by next equation: s k = (L 1) k p r (r j ) k = 0, 1, 2,..., L 1 (3) j=0 where s k 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 grayscale mapping provides similar enhancement for all regions of the image. However, when distributions of grayscales change from one region to another, this assumption is not valid. In this case, an adaptive histogram equalization technique can significantly outperform the standard approach. In this case, the image is divided into a limited number of regions and the same histogram equalization technique is applied to pixels in each region [9]. Even in some cases this method can not resolve the problem, when grayscale distribution is highly localized, it might not be desirable to transform very low-contrast images by full histogram equalization. In these cases, the mapping curve may include segments with high slopes, meaning that two very close grayscales might be mapped to significantly different grayscales. This issue is resolved by limiting the contrast that is allowed through histogram equalization. The combination of this limited contrast approach with the aforementioned adaptive histogram equalization results in what is referred to as Contrast Limited Adaptive Histogram Equalization (CLAHE) proposed in [10]. The CLAHE procedure consists in: First the image has to be divided into several nonoverlapping regions of almost equal sizes. Secondly the histogram of each region is calculated. Then, based on a desired limit for contrast expansion, a clip limit for clipping histograms is obtained. Next, each histogram is redistributed in such a way that its height does not go beyond the clip limit. The clip limit β is obtained by: β = MN ( 1 + α ) L 100 (s max 1) (4) where α is a clip factor, if clip factor is equal to zero the clip limit becomes exactly equal to ( MN L ), moreover if clip limit is equal to 100 the maximum allowable slope is s max.

3 Finally, cumulative distribution functions (CDF) of the resultant contrast limited histograms are determined for grayscale mapping. The pixels are mapped by linearly combining the results from the mappings of the four nearest regions; this process is explained in [11]. The Fig. 3 shows the differences among histograms of same image, applying both methods before mentioned CLAHE and Histogram Equalization. θ(u) = arctan [ ] I(u) R(u) This is also demonstrated by Oppenheimt s experiment shown in Fig. 4, in this experiment the Fourier Transform was applied to these two images and obtain the magnitude and phase. If combine the phase of the image 1 with the magnitude of the image 2 and the phase of the image 2 with the magnitude of the image 1, is prove that the component that provides more information about the image is the phase. (7) Fig. 4: Oppenheimt s experiment. Fig. 3: Comparison of CLAHE and Histogram Equalization. a) Original image and its histogram. b) CLAHE image dividing (a) in four regions of equal size and its histogram. c) Histogram equalization image and its histogram. 2.2 Phase Spectrum Oppenheim et al. [12] show that phase information retains the most part of the intelligibility of an image, because the phase spectrum contains most of the image information. This can be computed trough of a Fourier Transform which is given by: 2.3 Principal Components Analysis The PCA is a way of identifying patterns in data, and expressing the data in such a way as to highlight their similarities and differences. Since patterns in data can be hard to find in data of high dimension, where the luxury of graphical representation is not available, PCA is a powerful tool for analyzing data [13]. The other main advantage of PCA is that once these patterns are found in the data, and the data is compress, the number of dimensions are reduce, without much loss of information. F (u) = F (u) exp jθ(u) (5) where the magnitude is: and the phase is: F (u) = [R 2 (u) + I 2 (u)] 1 2 (6) Fig. 5: Feature extraction system by PCA. Fig. 5 shows the procedure for PCA application which is used in both phases training and recognition. The next procedure was used in training phase:

4 Firstly the training images are converted in column vectors, and then these vectors make a matrix (Principal Matrix). Next the principal components were extracted of the Principal Matrix to obtain a matrix of dominant features (D.F). Finally the vector of each person is multiplied by D.F to generate a feature vector, subsequently these vectors conform a matrix of feature vectors. This step is only used in the identification phase Support Vector Machine A support vector machine is basically a binary pattern classification method, whose objective is to assign each pattern to a class [14]. The SVM is used differently in each one of the phases using the vectors features obtained by PCA method. On training phase the SVM generates templates of each person, and in recognition phase decides whether feature vector agree or disagree with all templates. in conjunction with the obtain phase spectrum, the result is convert in vector for initialize the step of PCA. The first modification (CLAHE (2,2)) consists in apply the CLAHE dividing the original image in 2 parts in y axis and 2 in x axis generating four blocks, for later apply the Fast Fourier Transform (FFT) to obtain the phase spectrum of 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 one method proposed in [6] which used histogram Equalization in full image for after extract the phase spectrum also of full image (Full HE). Moreover is compared with the original Eigenphases method. The Fig. 8 shows the process of these two methods. Fig. 6: Decision step by SVM in identification phase. The decision step is shows in Fig. 6. The feature vector of the person to recognize is applied to all one vs. all SVMs. The class given the Maximum Likelihood is used as the person s identity; the equation to obtain the Maximum Likelihood is as follows: Ŝ = arg max 1 k S P (λ k x) (8) where Ŝ is the winner and thus revealing the person s identity to whom this image was assigned, x is the column vector of the image to analyze and λ k is the SVM model of the person k [15]. 2.5 Algorithm Variations As mentioned earlier the proposed system was compared with the original method of Eigenphases [5] and the based in Histogram Equalization [6], for this comparison there were performed three variations in the proposed algorithm. It is noteworthy that variations are on the pre-processing stage Fig. 8: Differences among CLAHE (2, 2) and Full HE (white lines are not part of the image, only used to illustrate the number of divisions used by CLAHE). The second variation (CLAHE (8,6)) is based on applying the CLAHE and dividing the original image in 8, 6 parts (y, x axes) generating 48 blocks each one of 6x6 pixels, next the Fast Fourier Transform is applied to extract the phase spectrum of full image, the Fig. 9 shows this process. Fig. 9: Second variant of system called CLAHE (8,6).

5 This method which compares the face image is firstly segmented in blocks of size 6x6. Next the histogram equalization is applied to each block, which concatenated to reconstruct the face image under analysis. Finally the Fourier Transform is applied to the whole image to estimate the phase spectrum (Local HE), as shown in Fig. 10. Fig. 12: Differences among Fourier CLAHE and Fourier HE (white lines are not part of the image, only used to illustrate the concatenation of the blocks by phase spectrum extracted). Fig. 10: Differences among CLAHE (8,6) and Local HE (white lines are not part of the image, only used to illustrate the 48 blocks used by CLAHE). The Fig. 11 shows the finally variation of the system, which consists on applying the CLAHE to form 48 blocks of 6x6 pixels as in the previous structure, next the Fast Fourier Transform is applied to each block, to estimate the phase spectrum of the face image, finally these blocks are concatenated to reconstruct the phase spectrum of the face image, which is obtained using the estimated phase of each block (Fourier CLAHE). conditions, using a face data base created in to the National Polytechnic Institute of Mexico which contains 2880 face images. This data base includes 24 face images of 120 subjects, 60 males and 60 females, under controlled conditions such as different illumination, facial expressions variations and partial occlusion using sunglasses, the size of the images is 480 x 360 pixels. As shown in Fig. 13. Fig. 11: Final variant of system called Fourier CLAHE. The final method is compared with its similar with HE this is shown in the Fig. 12, that is the same procedure as Local HE but the Fast Fourier Transform is applied to each block, to estimate the phase spectrum of the face image, and with these blocks which are concatenated to reconstruct the phase spectrum of the face image, which is obtained using the estimated phase of each block called Fourier HE. 3. EVALUATION RESULTS To evaluate the results of proposed and conventional methods, there have realized the tests under the same Fig. 13: Example of the face images in the database.

6 Six images were used for the training phase to generate a template for each person, all images are resized to 48 x 36 pixels, to calculate the PCA. The number of training images was calculated trying to get the best results with the least number of images possible. The results for obtaining the number of training images are shown in the graph on Fig. 14. Table 1: RESULTS WITH HE VARIATIONS AND EIGEN- PHASE METHOD Result % Eigenphases Full HE Local HE Fourier HE The Table 2 shows the results of CLAHE method and its variations. All these results are better than the obtained using the original method, Fourier CLAHE was the best of these results. Table 2: RESULTS WITH CLAHE VARIATIONS Result % CLAHE (2,2) CLAHE (8,6) Fourier CLAHE Fig. 14: Graph of results to determine the number of training images. 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 all database using the 24 face images of 120 people. Therefore in the following results there were used 6 images for training, these images shown in Fig. 15. The Fig. 16 shows the comparison among the results of Eigenphases with the best result of HE variation (Fourier HE) and the best of CLAHE variation (Fourier CLAHE). Fourier CLAHE is the best result of this comparison, providing accuracy identification from 97.36%. Fig. 16: Comparison among Fourier HE and Fourier CLAHE with the conventional method Eigenphases, using the same conditions. Fig. 15: Training images example. In the variations using HE and the original algorithm Eigenphases the results is shown in Table 1, in which it is noted that the best result is obtained by Fourier HE enhancement about 6 percentage points to the original method. 4. Conclusion The best identification result obtained in this paper is the "Fourier CLAHEŤ, surpassing 6% to conventional method Eigenphases [5] and a little " Fourier HE ", as seen in Figure 16. "Fourier HE" in turn is the best result obtained using Histogram Equalization variations proposed in [6]. It is important to mention that the 3 proposed variations using CLAHE improve the conventional method Eigenphases, in contrast to the 3 variations using HE as it "Full HE" presents a lower assertiveness than Eigenphases.

7 Moreover, the proposed system shown to be robust to changes in the database used, which are illumination changes and partial occlusion by using sunglasses, where the results obtained are greater than 90% and in the best cases obtained a 97.36% which is acceptable for a face recognition system. Acknowledgment Thanks to everyone who helped make this project, especially those who gave their time voluntarily for the realization of the database. References [1] S. Y. Kung, M. W. Mak and S. H. Lin, Biometric Authentication: A Machine Learning Approach, New York: Prentice Hall, 2005, vol. 3. [2] H. M. El-Bakry and N. Mastorakis, "Personal identification through biometric technology," 9th WSEAS International Conference on Applied Informatics and Communications (AIC 09), Moscow, Russia, pp , [3] Stan Z. Li and Anil K. Jain, Handbook of Face Recognition, Ed. Berlin, Germany: Springer-Verlag, [4] R. Chellapa, P. Sinha, P. J. Phillips, "Face recognition by computers and humans," Computer Magazine, Vol. 43, pp , Feb [5] M. Savvides, B. V. K. Vijaya Kumar, and P. K. Khosla, "Eigenphases vs. Eigenfaces," Proc. 17th International Conference on Pattern Recognition, ICPR, vol. 3, pp , Aug [6] K. Ramirez-Gutierrez, D. Cruz-Perez, J. Olivares-Mercado, M. Nakano- Miyatake, and H. Perez-Meana, "A Face Recognition Algorithm using Eigenphases and Histogram Equalization," International Journal of Computers Issue 1, Volume 5, pp , January, [7] J. Olivares-Mercado, K. Hotta, H. Takahashi, M. Nakano-Miyatake, K. Toscano-Medina, H. Perez-Meana, "Improving the eigenphase method for face recognition", IEICE Electronic Express, vol. 6, pp , June, [8] Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, 3th ed., Ed. Pearson Prentice Hall, [9] Ali M. Reza. "Realization of the Contrast Limited Adaptive Histogram Equalization (CLAHE) for Real-Time Image Enhancement." Jurnal of VLSI Signal Processing, vol. 38, pp 35-44, [10] S.M. Pizer, E.P. Amburn, J.D. Austin, R. Cromartie, A. Geselowitz, T. Greer, B.M. ter Haar Romeny, J.B. Zimmerman, and K. Zuiderveld, "Adaptive Histogram Equalization and its Variations," Computer Vision, Graphics and Image Processing, vol. 39, 1987, pp [11] K. Zuiderveld, Contrast Limited Adaptive Histogram Equalization, Heckbert., Ed. Cambridge, MA, Academic Press, [12] A. V. Oppenheim and J. S. Lim, "The importance of phase in signals," Proc. IEEE, vol 69, pp , May [13] Lindsay I Smith. "A tutorial on Principal Components Analysis." February 26, [14] W. M. Campbell, J. P. Campbell, D. A. Reynolds, E. Singer, y P. A. Torres-Carrasquillo. "Support vector machines for speaker and language recognition," Computer Speech and Language, Vol. 20, N. 2-3, pp , 2006b. [15] T. Glasmachers and C. Igel, "Maximum Likelihood Model Selection for 1-Norm Soft Margin SVMs with Multiple Parameters," IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 32, No. 8, pp , August 2010.