A Novel Unobtrusive Face and Hand Geometry Authentication System in .NET

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A Novel Unobtrusive Face and Hand Geometry Authentication System
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Figure 10.1. Color and depth face and hand images acquired using the 3D sensor (see
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Section 10.3). In the range image, brighter colors correspond to points closer to the camera, while white pixels correspond to undetermined depth values.
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images have a resolution of 780 580 pixels, while the depth accuracy of the 3D sensor is 0.5-mm standard deviation for objects standing at about 1-m distance in a working volume of 60 cm 40 cm 50 cm (width height depth) [5]. Due to the 3D acquisition principle, the acquired depth images may contain pixels where no depth values were computed. These holes are usually located over areas that cannot be reached by the projected light (e.g., the sides of the nose or the sides of the ngers) and/or over highly refractive (e.g., eyeglasses, rings, painted nails) or low re ective surfaces (e.g., hair, beard) (see Figure 10.1). The authentication system consists of the 3D sensor, a monitor and a PC, where the software runs. The user stands in front of the sensor looking at the camera so that her face is inside the effective working volume. The monitor shows a real-time video of the recorded color image sequence, while it also displays directions for correct placement of the face and hand inside the working volume by tracking the face and hand of the user. After a pair of face images is acquired, the user is asked to place her hand in front of her face with the back of the hand facing the sensor, keeping her ngers straight. This posture is most convenient for all users and provides the best resolution of hand images. After capturing several pairs of hand images, the system positively identi es or rejects the user based on fusion of the matching scores provided by the face and hand classi ers. Although there are obviously some limitations on the working conditions under which the system operates (e.g., large facial poses or large nger bending are not allowed), the proposed authentication system does not constrain user movement; at the same time, special care is taken to provide a user-friendly interface.
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In this section we describe the various components of the face authentication system. First, 3D data are exploited for the detection and localization of the face. Then, we compensate for the pose of the face and the illumination of the scene, thus generating frontal views with constant illumination. The normalized color and depth images are subsequently used for face classi cation. A block diagram illustrating the various steps of the face authentication algorithm is shown in Figure 10.2.
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10.4 3D Face Authentication System
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Figure 10.2. Block diagram illustrating the various steps of the user authentication algorithm based on color and depth facial images.
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Face Detection and Feature Localization
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Face detection and localization is based exclusively on 3D data, thus exhibiting robustness in illumination changes and occlusion of facial features. First, we detect the face in the 3D image using global descriptors and a priori knowledge of the geometry and relevant dimensions of the head and other body parts. Then, we localize the position of the nose using a knowledge-based technique. Segmentation of the body from the background is achieved using the histogram of depth values and estimating the threshold separating the two distinct modes. Segmentation of the head from the body relies on statistical modeling of the head torso points. The probability distribution of a 3D point x is modeled as a mixture of two Gaussians: P(x) = P(head)P(x|head) + P(torso)P(x|torso) (10.1) = 1 N(x;
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1, 1 ) + 2 N(x; 2, 2 ),
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where 1 , 2 are the prior probabilities of the head and torso, respectively, and N(x; , ) is the 3D Gaussian distribution with mean and covariance . Maximum-likelihood estimation of the unknown parameters k , k , k , k = 1, 2, from the 3D data is obtained by means of the expectation maximization algorithm [6]. To avoid the convergence of the algorithm to local minima, good initial parameter values are required, which may be obtained using 3D moments. Let m be the center of mass, let ST = n (xn m)(xn m)T be the scatter matrix computed from the data points xn , and let ui , i = 1, . . . , 3, be the eigenvectors of ST , ordered according to the magnitude of the corresponding eigenvalues i . Initial estimates of the unknown parameters are selected by 1 = m + 1 smin u1 , 2 = m + 2 smax u1 ,
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= diag( k 1 , k 2 , 3 ),
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k = k , where smin = min{(xn m)T u1 }, smax = max{(xn m)T u1 }.
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