Signature recognition

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

An Efficient Signature Recognition Based on Histogram Using Statistical Characteristics

Abstract: This paper presents an efficient signature recognition method by using the hybrid similarity criterion, which is in inverse proportion to distance and in proportion to correlation between the images. The distance is applied to express the spacial property of image, and the correlation is also applied to express the statistical property. The proposed criterion provides the robust recognition to both the geometric al variations such as position, size, and rotation and the shape variation. The normalized cross-correlation(NCC), which i s calculated by considering 4 directions based on the histogram of binary image, is applied to express rapidly and ac curately the similarity between the images. The proposed method has been applied to the problem for recognizing the 20 truck images of 288*288 pixels and the 105(3 persons * 35 images) signature images of 256*256 pixels, respectively. The experimental results show that the proposed method has a superior recognition performance that app ears the image characters well. Especially, the hybrid criterion of NCC and ordinal distance has a superior recognition performance to the hybrid criterion using city-block or Euclidean distance.Key Words : Signature recognition, Similarity criterion, Distance, Normali zed cross-correlation, Histogram of binary image

Enhancing off-line biometric signature verification using a fingerprint assessment approach

Abstract: A method designed for matching biometric fingerprint images is applied to the static/image-based ”off-line” human signature modality. Using a publically available signature dataset, the verification performance is compared against three existing static methods. Furthermore, verification is assessed using all four methods within a multi-classifier system. The results show the application of the fingerprint method leads to a comparable performance with existing methods and a significant improvement is achieved within a multi-classifier configuration.

Rotating machines fault identification using back-propagation artificial neural network

Abstract: The authors describe a newly developed technique and system for real-time monitoring and identification of machine condition The machine health identification process is mainly based on recognition and comparison of the real-time captured vibrational signature to its standard signature The features extraction of the vibrational signature uses the technique of higher order spectra analysis These signature features will then input to an artificial neural network (ANN) for recognition and identification The output of the neural network was trained to generate a healthy index that indicates the machine health condition A DSP56001 based digital signal processor is employed to implement the signal processing algorithms together with the artificial neural networks for real-time operation The authors briefly describe the methodology, system and vibrational signature recognition Very encouraging and successful results have been obtained and are presented and discussed >

Optimum Feature Selection for Signature Verification Using Different Classifiers

Abstract: ............................................................................................................................................................. 4 TABLE OF CONTENTS .............................................................................................................................................. 5 LIST OF TABLES ...................................................................................................................................................... 8 LIST OF FIGURES ..................................................................................................................................................... 9 INTRODUCTION .................................................................................................................................................... 11 1.1 OVERVIEW ......................................................................................................................................................... 11 1.2 OBJECTIVE OF THE DISSERTATION ................................................................................................................. 12 1.3 ORGANIZATION OF THE DISSERTATION ......................................................................................................... 12 LITERATURE REVIEW ............................................................................................................................................. 14 2.1 NEURAL NETWORKS APPROACH ................................................................................................................... 14 2.2 HIDDEN MARKOV MODELS APPROACH ......................................................................................................... 14 2.3 STATISTICAL APPROACH ................................................................................................................................ 15 2.4 STRUCTURAL APPROACH ............................................................................................................................... 15 2.5 WAVELETBASED APPROACH ........................................................................................................................ 16 BIOMETRICS ......................................................................................................................................................... 18 3.

Privacy security protection system based on behavior characteristics

Abstract: The invention discloses a privacy security protection system based on behavior characteristics. The privacy security protection system includes a foreground interactive subsystem, a handwritten signature recognition subsystem and a handwritten signature recognition subsystem. According to the invention, on one aspect, by means of the handwritten signature recognition technology, the handwritten signature matching authentication can be realized through the combination with the HMM-IMM-based trajectory capturing model; and on the other aspect, the privacy protection of an Android terminal is realized through signature eigenvalues, so that better experiences can be brought to users. By means of the well-designed man-machine interaction interface, the system flexible and easy use is guaranteed.