A Comparative Survey on Biometric Identity Authentication Techniques in .NET

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A Comparative Survey on Biometric Identity Authentication Techniques
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the author suggests breaking the convolutional block into two: the initial feature extraction layer and the nal feature extraction and classi cation layer. Replacing the second part with another type of classi er such as a nearest-neighbor classi er greatly reduces the training time for the NN when adding new classes. On the whole, the self-organizing map provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, which results in invariance to minor changes in the image samples. Also, the convolution NN provides for partial invariance to translation, rotation, scale and deformation. Substituting the Karhunen Loeve transform for the self-organizing map produced slightly worse results. With ve images per person from the ORL database, Back et al. [14] claim that the proposed method resulted in 3.8% error.
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3.6.4 A Local Global Graph Approach for Facial Expression Recognition
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Kakumanu and co-workers [15, 16] present a local global graph approach for recognizing facial expressions from static images irrespective of varying illumination, shadows, and cluttered backgrounds. Initially a neural network to detect skin in real-world images, based on color constancy method, is presented. Later, the local global graph (LGG) method is presented here for detecting faces and facial expressions with a maximum con dence from skin segmented images. The LGG approach rst extracts the most important facial features and then interrelates them for face and facial expressions; that is, it combines local information with the global information. Facial expression recognition from the detected face images is obtained by comparing the LG expression graphs with the existing LG expression models present in the LGG database. 3.6.4.1 Neural Network
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An NN is used for adapting skin color under various illumination conditions. First, the images are color-corrected based on the illuminant estimate of the NN. The skin regions in the NN-stabilized images are then detected using a simple threshold operation. The advantage of the NN method for color adaptation is that it does not have any inherent assumptions about the object surfaces in the image or the illumination sources because the input to the network is only the color from the image. Initially, the NN is trained so as to adapt to the skin color using random images from a database consisting of images collected under various illumination conditions both indoor and outdoor and containing skin colors of different ethnic groups. The color correction step assigns achromatic (gray) to skin pixels. The second skin detection stage classi es the skin and non-skin pixels using a threshold based on the achromatic value of the color-corrected images. It should be noted that building an accurate classi er that can detect all the skin types under all possible illuminations, shadows, cluttered backgrounds, and makeup, using visible spectrum, is still an unsolved question.
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3.6 Facial Biometric
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Local Region Graph (Facial Feature Region Graph)
A local graph is built to encode the spatial relationships of the line segments that are a result of a curve- tting procedure on the segmented regions produced by the neural network. Each facial region is represented by a local graph; and to identify the segmented regions as facial regions, we need to match the local graph of the segmented regions to the stored model local graphs in the database. It is also important to identify the geometrical positions and placements of the facial features along with identifying those features. The authors use the Voronoi tessellation method, the Delaunay triangulation, and the local global graphs as the basic representation of the image structure. Matching two graphs involves establishing point correspondences between two graph node sets that maximize the likelihood between two graphs, given the spatial constraints. The extra step needed for recognizing facial expressions is that we need to compare the image LG graph with the existing expression LG graphs. The proposed LGG method does not require any training samples. The graph method is invariant to scale, to rotations, and, to a certain extent, to pose, and it is shown to perform robustly under various illumination conditions when combined with the neural-constancy-based skin detection. Five facial features are used to represent an expression, and the authors propose to use other features such as furrows and cheeks, and they also indicate facial motion during an expression.
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