Preprocessing in .NET

Printer qr codes in .NET Preprocessing
Preprocessing
.net Framework qr readerin .net
Using Barcode Control SDK for .net vs 2010 Control to generate, create, read, scan barcode image in .net vs 2010 applications.
Alignment Before matching (B), During matching (D) Displacement (D), Rotation (R), Scale (S), Non-linear (N) Minutiae
Denso QR Bar Code generating in .net
use .net qr codes implement torender qr code in .net
Features used Raw/Enh. image parts Local ridge frequency
Quick Response Code barcode library for .net
Using Barcode reader for .net vs 2010 Control to read, scan read, scan image in .net vs 2010 applications.
Comparison based on Ridge pattern (geometry) Ridge pattern (texture) x x x x x
VS .NET barcode decoderon .net
Using Barcode reader for Visual Studio .NET Control to read, scan read, scan image in Visual Studio .NET applications.
Minutiae (global)
.NET barcode generatingin .net
using .net vs 2010 tobuild bar code for asp.net web,windows application
Texture measures
Control qrcode size on .net c#
to add qrcode and qr-code data, size, image with c# barcode sdk
Orientation field
Qrcode barcode library in .net
generate, create qr codes none with .net projects
Minutiae (local)
Draw qr code 2d barcode in visual basic
use .net vs 2010 qr-code implement topaint qrcode with vb.net
Singular points
UPC Symbol barcode library on .net
generate, create upc a none on .net projects
Segmentation
Barcode barcode library for .net
use visual .net crystal bar code printer toget bar code in .net
Enhancement
Barcode Data Matrix barcode library for .net
using barcode development for .net framework crystal control to generate, create gs1 datamatrix barcode image in .net framework crystal applications.
Ridge counts
Leitcode barcode library for .net
using .net framework toembed leitcode for asp.net web,windows application
P039 x P047 x P071 P075 x P101 (x)
GTIN - 12 barcode library with .net
use visual studio .net (winforms) upc-a supplement 5 integrated toinsert upc a for .net
x x x x
Barcode Pdf417 generator on .net
using barcode creation for web pages control to generate, create barcode pdf417 image in web pages applications.
D D D B BD
Barcode Pdf417 printing for excel spreadsheets
using barcode implementation for microsoft excel control to generate, create pdf 417 image in microsoft excel applications.
N DRSN N DR DRS
Code 3/9 barcode library for vb.net
using .net tocompose barcode code39 on asp.net web,windows application
x x x x
Integrate bar code with .net
using barcode drawer for aspx.cs page control to generate, create bar code image in aspx.cs page applications.
x x x
Java bar code scannerfor java
Using Barcode recognizer for Java Control to read, scan read, scan image in Java applications.
x x x
Linear barcode library on word
generate, create 1d barcode none for word projects
x x x
GTIN - 12 barcode library in visual c#
use .net upc code creation tointegrate upc a in c#.net
x x x x
x x x
x x x
Figure 14.11. High-level description of the matchers cited in Table 14.5. Note about P101:
Segmentation is performed only on DB1 images.
Correlation
Algorithm
Ridges
14
Learning in Fingerprints
Table 14.6. Performance of Our Minutiae-Based Multimatcher MM(2) DB1 DB2 DB3 DB4 AUC EER AUC EER AUC EER AUC EER 0.985 3.8% 0.9975 1.52% 0.976 5.4% 0.9787 6.4% MM(3) 0.9855 3.8% 0.9964 1.52% 0.9751 5.28% 0.9809 6.4% MM(4) 0.9858 4% 0.9974 1.6% 0.9736 5.86% 0.9841 5.78% TICO 0.98 4% 0.9948 1.6% 0.968 6.5% 0.9765 7.7%
The authors of this chapter have studied the performance of the combination of minutiae matchers based on extended minutia descriptors: In particular minutiae, data (x, y coordinates and angles) are enriched with local orientation extracted from a neighborhood of each minutia as originally proposed by Tico and Kousmanen [66]. Each single matcher, implemented as described in reference 66, works on a different image: In fact, from the original gray-level image 17 images are extracted, the rst 16 are obtained by the wavelet decomposition of the original image, while the 17th image is a block frequency image (it encodes the local ridge density). Different wavelet families have been used: Haar, Daubechies order 4, Symmlet order 2, Coi ets order 2. Before combining the matchers through the sum rule, a feature selection has been performed on a disjoint data set using SFFS. Experiments have been conducted on the four FVC2002 databases according to the FVC2002 testing protocol. The equal error rate (EER) and AUC obtained by the following methods are reported in Table 14.6, where TICO denotes the original Tico and Kousmanen method as proposed in reference 66, and MM(n) denotes the proposed ensemble of matchers, where n is the number of matchers selected through SFFS. From the results it can be concluded that MM(2), MM(3), and MM(4) outperform TICO; the performance of all MM(n) are very similar, and therefore MM(2) is preferable because of the lower complexity; the two images selected for MM(2) are the horizontal coef cients of the Haar wavelet and the frequency image (as reported in Figure 14.12).
FINGERPRINT CLASSIFICATION
Fingerprint exclusive classi cation consists in assigning a ngerprint to a prede ned class and can be very useful for ngerprint identi cation to reduce the retrieval time and complexity by narrowing the search space to a subset of a potentially huge database. In fact, to speed-up the search in a ngerprint database, an initial coarse level selection (usually based on macro-features extracted from the ngerprint pattern) allows us to limit the accurate but also computationally demanding minutiae matching to the samples passing the initial selection.
14.6 Fingerprint Classi cation
Figure 14.12. (a) Enhanced ngerprint, (b) One-level decomposition by the Haar wavelet of (a). (c) Frequency image of (a).
All the exclusive classi cation schemes currently used by police agencies and automated systems for the coarse level search of database are variants of the so-called Henry s classi cation scheme (see Figure 14.13 for an example of each class). The natural ngerprint distribution of the Henry ve classes is 3.7% plain arch, 2.9% tented arch, 33.8% left loop, 31.7% right loop, and 27.9% whorl. Due to the small number of classes and the unevenly distribution among them, exclusive classi cation cannot suf ciently narrow down the search of database; therefore, continuous
Figure 14.13. The ve commonly used ngerprint classes marked with core (O) and delta ( )
points.
14
Learning in Fingerprints
classi cation and other indexing technique has been proposed for the coarse level search [89][92][93]; anyway, these are not a focus of the present work. Completely automated ngerprint exclusive classi cation is a dif cult pattern recognition problem, due to the small inter-class variability, the large intra-class variability, and the presence of noise, which has attracted the interest of many researchers during the last 30 years (see references 67 and 68 for a survey). Because of the complexity and variability of the ngerprint pattern, the approaches based on modeling or rule-based description of the ngerprint classes proved to be unable to deal with dif cult cases, and best results have been achieved with methods implementing learning by examples paradigms. Learning-based approaches will be reviewed in the following: r Syntactic Methods. A syntactic method describes patterns by means of terminal symbols and production rules of a grammar. Moayer and Fu [69] associate terminal symbols to small groups of directional elements within the ngerprint directional image and use a class of context-free grammars to describe the ngerprint patterns. Learning is here implicit in the grammar inference process. r Approaches Based on Ridge-Line Shape. These approaches [70, 71] extract and encode the ngerprint ridge structures and use these features as the basis for classi cation. Senior [71] trained a Hidden Markov Model classi er, whose input features are measured at the intersection points between some horizontal/vertical ducial lines and the ngerprint ridge-lines (ridge angle, separation, curvature, etc.). r Neural Network Approaches. Neural networks have been applied to ngerprint classi cation since the early 1990s and are generally based on the elements of the directional image [72 75]. Kamijo [75] proposes a pyramidal architecture constituted by several multilayer perceptrons, each of which is trained to recognize a different class. The well-known PCASYS system (Pattern-level Classi cation Automation System for Fingerprints) [73] developed by Candela et al. from NIST is an hybrid system that uses neural network classi cation followed by an auxiliary classi er (pseudoridge tracer) used to improve the reliability of classi cation. The directional image is rst registered with respect to the centre of the ngerprint image, then its dimensionality is reduced by PCA and classi ed by a probabilistic neural network (PNN). r Other Approaches. Several systems are based on clustering or general-purpose classi ers. Wang et al. [76] use a k-means clustering algorithm coupled to a three-nearest neighbors classi er. Cappelli et al. [77] use a MKL classi er [78] applied to an enhanced version of the orientation image (see Figure 14.14). Tan and Bhanu [79] propose an original approach based on feature-learning, where a set of arti cial features are learned and classi ed by a Bayesian classi er. Genetic programming is used to discover evolved features that are obtained from combinations of primitive image processing operations. r Combined Approaches. Recently, some researchers [80 82] have proposed the combination of different approaches to exploit their complementarities.