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Figure 14.4. Fingerprint segmentation tree proposed in reference 25.
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Figure 14.5. Orientation correctness evaluation. (a) Estimating orientation eld. (b) Three-layer
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neural network, (c) Black blocks indicate a correct orientation, and white blocks indicate incorrect orientation. (From reference 25. Copyright 2006, Elsevier.)
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are extracted from each block: seven of them are independent of the orientation and are used for distinguishing ridge blocks from non-ridge blocks, and the remaining four are used for distinguishing between correct and incorrect orientation. A three-layer perceptron network (Figure 14.5) is used to learn the correctness of an estimated local orientation. The network is also able to correct the wrongly estimated ridge orientation of a block using the orientation of the valid neighboring blocks. 3. Coarse Segmentation: Each image block is classi ed into foreground or background according to its orientation correctness. 4. Secondary Segmentation: Further classi cation of the foreground produced by the coarse segmentation based on the consideration that the gray difference between the ridges and valleys for remaining ridge blocks is usually smaller than for true ridge blocks. To this purpose, a set of four features are extracted for each block and fed to a linear classi er. 5. Orientation Correction and Segmentation Revision: Postprocessing phase that allows us to move blocks from foreground to background and vice versa based on heuristic rules (e.g., consistency of a block with its neighboring blocks).
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The performance of a ngerprint identi cation system heavily relies on the quality of the ridge valley structures of the input ngerprint. Unfortunately, due to different skin conditions, sensor noise, incorrect nger pressure, and inherently low-quality ngerprints, several images contain poor-quality regions where the ridge pattern is very noisy and corrupted. The aim of an enhancement algorithm is to improve the clarity of the ridge structures, thus making the successive feature extraction more reliable. The most effective ngerprint enhancement approaches proposed in the literature [1] are based on contextual ltering where the image is locally convolved with lters tuned according to the local context (i.e., ridge orientation and frequency). These are quite standard image processing techniques where the use of learning is typically limited to an initial tuning of the system aimed at nding out the best parameters. However, some isolated attempts have been introduced where the role of learning is
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Figure 14.6. (a) A noisy ngerprint image. (b) Expert-provided corresponding ridge map. (From
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reference 26. Copyright 2000, IEEE.)
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central: The main drawback of these approaches is to collect a reliable training set with proper ground truth. In the Ghosal et al. method [26] the lter coef cients are learned through a learnby-example paradigm from a small set of training ngerprints and the corresponding set of binary ridge maps manually drawn by an expert (see Figure 14.6). Bal et al. [27] proposed a supervised ltering technique that makes use of a recurrent neural network. Supervised ltering employs a xed-sized lter mask and a convolution operation, as shown in Figure 14.7, where hi,j are the weights of the lter mask, b is a scalar bias value, f is a xed nonlinear activation function, t is the iteration step, and am,n (t) is the pixel intensity at the step t. The recurrent ow of supervised ltering is described by the convolution between the input and the lter mask and then by a summation of the bias scalar value in the nonlinear activation function. The learning process consists in nding the optimal values for the weights hi,j and the bias value b that minimize the difference between the current output and the desired output.
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Minutiae (or Galton characteristics) are essentially the terminations and bifurcations of the ridges in a ngerprint image (Figure 14.8). Because of their high discriminant power, minutiae are widely used in automatic ngerprint recognition systems.
b p a(t) h11 h12 h13 h21 h22 h23 h31 h32 h33 c(t) + f(-) a(t+1)
Figure 14.7. Supervised ltering architecture proposed by Bal et al. [27].