Signature recognition

About: Signature recognition is a research topic. Over the lifetime, 2138 publications have been published within this topic receiving 37605 citations.

Handwritten Chinese Character Recognition Using Modified LDA and Kernel FDA

Abstract: The effectiveness of kernel fisher discrimination analysis (KFDA) has been demonstrated by many pattern recognition applications. However, due to the large size of Gram matrix to be trained, how to use KFDA to solve large vocabulary pattern recognition task such as Chinese Characters recognition is still a challenging problem. In this paper, a two-stage KFDA approach is presented for handwritten Chinese character recognition. In the first stage, a new modified linear discriminant analysis method is developed to get the recognition candidates. In the second stage, KFDA is used to determine the final recognition result. Experiments on 1034 categories of Chinese character from 120 sets of handwriting samples shows that a 3.37% improvement of recognition rate is obtained, which suggests the effectiveness of the proposed method.

On-Line Signature Recognition Based on Reduced Set of Points

Abstract: In the paper two methods of signature points reduction are presented. The reduction is based on selecting signature’s characteristic points. The first method is based on seeking points of the highest curvature using the IPAN99 algorithm. Parameters of the algorithm are selected automatically for each signature. The second method uses a comparative analysis of equal ranges of points in each signature. For both of methods the way of determination of characteristic points has been shown. As a result of experiments carried out the effectiveness of both methods and its usefulness for signature recognition and verification has been presented.

Face Recognition Technology: A Biometric Solution to Security Problems

Abstract: Face recognition technology is one of the most widely used problems in computer vision. It is widely used in applications related to security and human-computer interfaces. The two reasons for this are the wide range of commercial and law enforcement applications and the availability of feasible technologies. In this chapter the various biometric systems and the commonly used techniques of face recognition, Feature Based, eigenface based, Line Based Approach and Local Feature Analysis are explained along with the results. A performance comparison of these algorithms is also given. This chapter appears in the book, Multimedia Systems and Content-Based Image Retrieval, edited by Sagarmay Deb. Copyright © 2004, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. 701 E. Chocolate Avenue, Suite 200, Hershey PA 17033-1240, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.idea-group.com IDEA GROUP PUBLISHING Face Recognition Technology: A Biometric Solution 63 Copyright © 2004, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. INTRODUCTION Biometrics is defined as the automated use of physiological or behavioral characteristics to determine or verify an identity. A biometric device compares unique personal characteristics to identify the individuals. The two major categories of biometric devices are Physiological and Behavioral. Physiological biometric identification measures unique body characteristics such as fingerprint details, retina blood vessel patterns, features of the iris, the size and shape of a hand or facial scan. It compares these characteristics against a pattern recorded during an enrollment process. Behavioral measurements identify unique learned traits such as a person’s signature, voice scan, and keystrokes scan. The major biometric technologies that are being used nowadays are: • Finger scan • Iris scan • Hand scan • Voice scan • Retina scan • Signature scan • Facial scan It is clear that the events of September 11 had a profound effect on security-based systems. Clearly, the recent events will have a significant impact on the future demand in the biometrics industry. In this chapter, the main emphasis is on “facial scan or face recognition.” Face recognition is distinguishing people’s faces. Humans have the capability to recognize faces. A large database of human faces is stored in our brain and to identify any face the face of the person is matched with the face database of persons stored in our memory. If a successful result is obtained, a person recalls the identity of the face or else it is added into the database of faces in the brain. This performance is related to neurons and all but actually what happens in the brain during recognition is still not clear. Here, a brief description of the major biometric technologies is given. Finger-Scan Technology Finger-scan biometrics is based on the distinctive characteristics of the human fingerprint. A fingerprint image is read from a capture device, features are extracted from the image, and a template is created. If appropriate precautions are followed, what results is a very accurate means of authentication. Following are the terminology for the method: Fingerprints vs. Finger-scans — Fingerprint Characteristics — Feature Extraction — Silicon, Optical, Ultrasound Fingerprints vs. Finger-Scans The aura of criminality that accompanies the term “fingerprint” has not significantly impeded the acceptance of finger-scan technology, because the two authentication methods are very different. Fingerprinting, as the name suggests, is the acquisition and 37 more pages are available in the full version of this document, which may be purchased using the "Add to Cart" button on the publisher's webpage: www.igi-global.com/chapter/face-recognitiontechnology/27055

Pattern recognition based on information theory principles

Abstract: One of the main problems faced in the development of pattern recognition algorithms is assessment of their performance. This paper describes the development of a novel technique for the assessment of information content of 2-D patterns encountered in practical pattern recognition problems. The technique is demonstrated by itsapplication to multi-font typed character recognition. In this work we firstly developed an information model applicable to any pattern, and its elaboration to measure recognition performance, and secondly we used this model to derive parameters such as the resolution required to distinguish between the patterns. This has resulted in a powerful method for assessing the perfoimance of any pattern recognition system.Keywords: pattern recognition, information theory, character recognition, recognition information 1. INTRODUCTION Pattern Recognition is one of the fastest growing scientific areas with applications across a wide variety of disciplines. The tasks of pattern recognition are basically to remove the need for a trained operator to perform therecognition, or to enable recognition to be performed that would otherwise be impossible [1]. When examining a

The method for determining the characteristic points of signatures based on IPAN99 algorithm

Abstract: The paper puts forward a new method of determination of signatures’ characteristic points. The method is based on seeking points of the highest curvature using th e IPAN99 algorithm. The way of IPAN99 algorithm parameters’ automatic selection for a particular signature has been fully described. Moreover, the way of determin ation of additional characteristic points, important for a signatures a nalysis, has been shown. The presented results of c arried out experiments confirm that the proposed method is use ful for signature recognition and verification.