CHAPTER 5 SENDER AUTHENTICATION USING FACE BIOMETRICS


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1 74 CHAPTER 5 SENDER AUTHENTICATION USING FACE BIOMETRICS 5.1 INTRODUCTION Face recognition has become very popular in recent years, and is used in many biometricbased security systems. Face recognition is a general topic that includes both face identification and face authentication or verification (Mohamed et al 2010). On one hand, face authentication is concerned with validating a claimed identity based on the image of a face, and either accepting or rejecting the identity claim. On the other, the goal of face identification is to identify a person based on the image of a face. This face image has to be compared with all the registered persons. Thus, the key issues in face recognition are to extract the meaningful features that characterize a human face, and then recognize it. Some facial recognition algorithms identify faces by extracting landmarks, or features, from an image of the subject's face. For example, an algorithm may analyze the relative position, size, and/or shape of the eyes, nose, cheekbones, and jaw. These features are then used to search for other images with matching features. Other algorithms normalize a gallery of face images and then compress the face data, saving only the data in the image that is useful for face detection. A probe image is then compared with the face data. Popular recognition algorithms include Principal Component Analysis (Turk and Pentland 1991 and Abdi and Williams 2010) using Eigen faces, Linear Discriminate Analysis (Martinez and Kak 2001), Elastic Bunch Graph Matching (Seiichi and Hiroaki 2005) using the Fisher face algorithm
2 75 (Peter et al 1997), and the neuronal motivated dynamic link matching ( In this work, the Eigen face based recognition algorithm is used along with the color based technique for face detection for sender authentication. These face detection algorithm and Eigen face based face recognition algorithm, are explained in the following sections. 5.2 DETECTION AND PREPROCESSING OF A FACE The first step of any fully automatic facial recognition system is face detection. Face detection (Prem et al 2002) is the process of finding whether or not there are any faces in a given image, and if present, the location and content of the face image are returned. Real world images need not necessarily contain isolated faces that can directly serve as inputs to a face recognition system, and hence, there is a need to isolate or segment facial regions from the background. A color based technique is implemented for detecting human faces in images. The method consists of two image processing steps. First, the skin regions are separated from the nonskin regions. After that, the human face within the skin regions is located and cropped. In order to segment the human skin regions from the nonskin regions based on color, a reliable skin color model of different people is needed ( ~robles/ee368/main.html). Luminance can be removed from the RGB color representation in chromatic color space. Chromatic colors such as Red (r), Blue (b) and Green (g) are defined by a normalization process as given in the Equation (5.1): r = R/(R+G+B) and b = B/(R+G+B) (5.1) where R, G and B are the red, green, and blue components of the image before color balancing, r, g and b are the color balanced red, green, and blue components of a pixel in the image, and the green color is redundant after the
3 76 normalization because r + g + b = 1. Our system uses a face library of sixteen facial images as shown in Figure 5.1 and these images are taken under controlled environment. Hundred and sixty skin samples from these sixteen color images are used to determine the color distribution of the human skin in chromatic color space. The color samples are shown in Figure 5.2. As the skin samples are extracted from the color images, the skin samples are filtered using a lowpass filter, to reduce the effect of noise in the samples. Figure 5.3 shows the color distribution of these skin samples in chromatic color space. Figure 5.1 Sample Face Image Database
4 77 Figure 5.2 Color Samples from the Face Image Database Figure 5.3 Color Distributions for Skincolor of Different People The color histogram revealed that the distributions of the skincolor of different people are clustered in chromatic color space, and a skin color distribution can be represented by a Gaussian model N (m, C), where: Mean, m = E {x} where x = (r, b) T (5.2) Covariance, C = E {(x  m) (x  m) T } (5.3) Figure 5.4. The Gaussian distribution, N (m, C) fitted by our data is shown in
5 78 With this Gaussian fitted skin color model, the likelihood of skin is obtained for any pixel of an image. If a pixel, having transformed from RGB color space to chromatic color space, has a chromatic pair value of (r, b), the likelihood of skin for this pixel can be computed as follows: Likelihood = P (r, b) = exp [0.5 (xm) T C 1 (xm)] (5.4) where x = (r, b) T, m = mean, C = Covariance, r = red and b = blue Figure 5.4 Gaussian Distribution from Skin Color Hence, this skin color model can transform a color image into a gray scale image such that the gray value at each pixel shows the likelihood of the pixel belonging to the skin. The skinlikelihood image will be a grayscale image whose gray values represent the likelihood of the pixel belonging to a skin. Since the skin regions are brighter than the other parts of the images, the skin segmentation can be done from the rest of the image through a
6 79 thresholding process. Since people with different skins have different likelihoods, an adaptive thresholding process is required to achieve the optimal threshold value for each image. Using this technique, the skincolored regions are effectively segmented from the nonskin colored regions. A skin region is defined as a closed region or a set of connected components within the image, which can have 0, 1 or more holes inside it. Its color boundary is represented by pixels with value 1 for binary images. All holes in a binary image have pixel value of zero (black). A sample color image, its resulting skinlikelihood image, the skin segmented image and the actual skin region are shown in Figure 5.5.a, b, c and d. a b c d Figure 5.5 Processing of the Original Face Image to get the Actual Skin Region To study the face region, its area and center are to be determined first. One efficient way is to compute the center of mass (i.e., centroid) of the region (Zhili and Chunhung 2006). The center of the area in binary images is the same as the center of the mass, and it is computed as shown below: n m 1 x j B [i, j] A i 1 j 1 (5.5) n m 1 y i B [i, j] A i 1 j 1 (5.6)
7 80 where B is the matrix of the size [n x m] representation of the region, and A is the area in pixels of the region. In that way, the center point (x, y) of the actual face region is found and is shown in Figure 5.6.a and b. a b Figure 5.6 Center Point in the Skin Region and in the Original Image Finally, using this center point the actual face region is cropped, normalized, and is shown in Figure 5.7. By using the same method, all the sample images are cropped and normalized, and are shown in Figure 5.8. Figure 5.7 Cropped and Normalized Face Region
8 81 Figure 5.8 Cropped and Normalized Face Images 5.3 EIGEN FACE CREATION Biometric authentication systems such as face recognition systems, are being actively investigated for access control and security applications. However, there are many issues that need to be addressed to ensure the security of biometric templates. One such aspect is the cancelability or revocability of a biometric (Marios et al 2004). In order to protect the user s
9 82 biometric templates from possible hacking and to ensure cancelability, the templates have to be changed in their form by applying a transformation function. Then in case of theft or loss, a different biometric template can be issued from the same original biometric by applying a different function. Eigen faces are a set of eigenvectors used for human face recognition. A set of Eigen faces can be generated by performing a mathematical process, called the Principal Component Analysis (PCA) (Turk and Pentland and Ramesh et al 1995) on a large set of human face images. The Eigen faces will appear as light and dark areas that are arranged in a specific pattern. This pattern shows how different features of a face are singled out to be evaluated and scored. The Eigen face approach is applied to the facial images of our database to recognize someone's face. The problem is to be able to accurately recognize a person's identity and allow the person to access highly secure information. The procedure involved in Eigen face creation is explained as follows: The cropped and normalized images are placed into the training set S and are resampled to the same pixel resolution. Each image is treated as one vector, simply by concatenating the rows of pixels in the original image, resulting in a single row with r c elements. The facial images shown in Figure 5.8 are converted into gray images, resampled and are used as the input for this method. In our example M = 16 and all images are transformed into a vector of size N and placed into the set S. S = { 1, 2, 3,... M} (5.7) and The training set and the normalized set are shown in Figures 5.9
10 83 Figure 5.9 Training Set Gray image Figure 5.10 Normalized Training Set
11 84 After that, the mean image is created by using the following Equation (5.8) and is shown in Figure M m n 1 n (5.8) Figure 5.11 Mean Image Then the difference between the input image and the mean image is found by using the Equation (5.9) i = i  (5.9) Next a set of M orthonormal vectors, u n, is found, which best describes the distribution of the data. The k th vector, u k, is chosen such that k 1 M m T 2 (u n 1 k n) (5.10) is the maximum, subject to u u T l k lk 1 if l k 0 otherwise u k and k are the Eigenvectors and Eigenvalues of the covariance matrix C and is calculated by using the Equation (5.11)
12 85 C 1 M m T T n 1 n n AA where A={ 1, 2,... n } (5.11) Since the C matrix is an N 2 x N 2 matrix, computing its eigenvectors is not computationally feasible. Instead, the eigenvectors v l of the new matrix L = A T A are found, which has the same eigenvectors of the matrix C = AA T L mn = T m n (5.12) Once the eigenvectors, v l, of the L matrix are found, the Eigen faces u l can also be found by the following Equation (5.13) and the Eigen faces of the original images are shown in Figure m u v l 1...,M (5.13) l lk k k 1 Figure 5.12 Eigen Faces
13 EIGEN FACE RECOGNITION SYSTEM At the receiving end, from the received facial image of the sender, the Eigen face component is generated, and it needs to be verified by the receiver, as to whether the Eigen face belongs to the given training set or not. In that way the receiver ensures that the message is sent only by the genuine sender, and not by a deceitful one. The recognition system involves the following steps: 1. First the Eigen face component is compared with the mean image and their difference is multiplied by each eigenvector. Each value would represent a weight, and would be saved on a vector. k u ( ) (5.14) T k where = weight, = eigenvector, = input image, = mean face. The weight vector is given by T [ 1, 2,..., M ] (5.15) 2. Then the face class which provides the best description for the input image is determined by minimizing the Euclidean distance k 2 k (5.16) 3. So, for a new face input, say k to be verified, the average weight vector, k th facial image s weight vector k and the Euclidean distance for this k th face k are found.
14 87 4. If this Euclidean distance k is less than an established threshold value, then the face image is considered to be a known face and it belongs to the training class. If the distance is above the given threshold, but below a second threshold, the image can be determined as an unknown face. If the input image is above these two thresholds, the image is determined not to be a face (Turk and Pentland 1991). 5.5 IMAGE RANDOMIZATION The facial images are collected at the leader of the group and once the mean image is found out, it is distributed to all the users. The same is done to all the leaders also. When user1 of group A sends data to his leader, the facial image of user1 is randomized using the image randomizer. Then, these randomized images are appended with the plain text, and sent to the receiver. In our work, the sender s facial image is divided into 16 pieces, and is randomized as shown in Figure At the receiving end, the decrypted images are reassembled and verified with the mean image. Figure 5.13 Sender s Facial Image is Divided and Randomized
15 RESULTS In this experiment, the threshold values of the Euclidean distance of a different number of faces are tested. The face library that is used for our experiment contains 32 face images in which 16 are genuine users images, and 16 are fake users images. All 16 genuine images are in the training set. Then, the Euclidean distance of every image of the face library is found out, and Figure 5.14 is plotted using these Euclidean distances. Figure 5.14 Recognition of the Genuine Users using the Euclidean Distance In Figure 5.14, the genuine users Euclidean distances are shown in blue color, and the fake users Euclidean distances are shown in red. It is clearly seen that all the genuine users images have Euclidean distances less than the threshold value of 300. It is also seen that all 16 genuine users are classified correctly, and all 16 fake users are not classified by this method. Though the system is tested for 16 users it can be extended for any number of users. Chapter 6 describes the preprocessing of voice biometrics and Euclidean distance generation. The chapter also explains how voice biometrics is used for receiver authorization in hierarchical MANETs.
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