Evaluation of Pattern Classifiers for Fingerprint and OCR Applications

(Also cross-referenced as CAR-TR-691) In this paper we evaluate the classification accuracy of four statistical and three neural network classifiers for two image based pattern classification problems. These are fingerprint classification and optical character recognition (OCR) for isolated handprinted digits. The evaluation results reported here should be useful for designers of practical systems for these two important commercial applications. For the OCR problem, the Karhunen-Loeve (K-L) transform of the images is used to generate the inp ut feature set. Similarly for the fingerprint problem, the K-L transform of the ridge directions is used to generate the input feature set. The statistical classifiers used were Euclidean minimum distance, quadratic minimum distance, normal, and knearest neighbor. The neural network classifiers used were multilayer perceptron, radial basis function, and probabilistic. The OCR data consisted of 7,480 digit images for training and 23,140 digit images for testing. The fingerprint data consisted of 9,000 trai ning and 2,000 testing images. In addition to evaluation for accuracy, the multilayer perceptron and radial basis function networks were evaluated for size and generalization capability. For the evaluated datasets the best accuracy obtained for either pro blem was provided by the probabilistic neural network, where the minimum classification error was 2.5% for OCR and 7.2% for fingerprints

Distortion-Tolerant Filter for Elastic-Distorted Fingerprint Matching

This paper gives results for using distortion tolerant filters to improve performance of fingerprint correlation matching. Three types of distortion tolerant filters were tested: summation, weighted, and MINACE. A set of 55 fingers were used from NIST Special Database 24 to evaluate the filters. Our results show performance was improved from 49 % correct, using one training fingerprint, to 100 % correct, using multiple training fingerprints and a distortion-tolerant MINACE filter, with no false alarms. Keywords: Distortion-tolerant filter, elastic distortion, fingerprint, MINACE, NIST Special Database 24 1

Comparison of FFT Fingerprint Filtering Methods for Neural Network Classification

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2 Experimental Fingerprint Database . . . . . . . . . . . . . . . . . . . . . . . . . 6 3 Image Segmenting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 4 Fingerprint Image Enhancement . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 4.1 Localized FFT Fingerprint Filter . . . . . . . . . . 12 4.2 Directional FFT Filter . . . . . . . . . . . . 14 5 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 6 PNN Classifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 7 Method of Rejection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 8 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 8.1 Accuracy . . . . . . . . . . . . . . 23 8.2 Speed . . . . . . . . . . . . . . . 23 9 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . ..

S.: Fast pattern selection for support vector classifiers, Lecture

Abstract. Training SVM requires large memory and long cpu time when the pattern set is large. To alleviate the computational burden in SVM training, we propose a fast preprocessing algorithm which selects only the patterns near the decision boundary. Preliminary simulation results were promising: Up to two orders of magnitude, training time reduction was achieved including the preprocessing, without any loss in classification accuracies.

Quality Measures in Biometric Systems

This is an excerpt from the content Synonyms Quality assessment; Biometric quality; Quality-based processing Definition Since the establishment of biometrics as a specific research area in the late 1990s, the biometric community has focused its efforts in the development of accurate recognition algorithms [1]. Nowadays, biometric recognition is a mature technology that is used in many applications, offering greater security and convenience than traditional methods of personal recognition [2]. During the past few years, biometric quality measurement has become an important concern after a number of studies and technology benchmarks that demonstrate how performance of biometric systems is heavily affected by the quality of biometric signals [3]. This operationally important step has been nevertheless under-researched compared to the primary feature extraction and pattern recognition tasks [4]. One of the main challenges facing biometric technologies is performance degradation in less controlled situations, and the problem of biometric quality measurement has arisen even stronger with the proliferation of portable handheld devices, with at-a-distance and on-the-move acquisition capabilities. These will require robust algorithms capable of handling a range of changing characteristics [2]. Another important example is forensics, in which intrinsic operational factors further degrade recognition performance. There are number of factors that can affect the quality of biometric signals, and there are numerous roles of a quality measure in the context of biometric systems. This section summarizes the state of the art in the biometric quality problem, giving an overall framework of the different challenges involved