Signature recognition

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

A signature identification system with principal component analysis and stentiford thinning algorithms

Abstract: Several biometric security systems have been implemented. Biometric is the use of a person’s physiological or behavioural characteristics to identify the individual. An example of behavioural method of biometric is signature identification. Signature identification is the use of handwritten signature to identify a person. This paper attempt design and implement an algorithm for handwritten signature identification. The signature identification system consists of signature acquisition, preprocessing, features extraction and matching stages. Signature acquisition can be either online or offline (both were considered in this research work). Online signatures are obtained by signing on digital tablets while offline signatures are scanned (or snapped) into the system. Preprocessing stage of the system include turning the image to greyscale. The grey image is further converted to binary (black and white). The image is then thinned, using Stentiford thinning algorithm. Stentiford thinning algorithm in an iterative thinning method with a good thinned imaged output. The image is finally cropped to rid the image of unnecessary white spaces. For features extraction, principal component analysis is used. Principal Component Analysis is a good statistical tool for identifying pattern in data. Features extracted from each signature are stored as a template. After features extraction, the distance between signature templates are computed using Manhattan distance. If the distance exceeds a certain threshold, the test signature is rejected (otherwise it is accepted). The design system has a FAR of 4% and an FRR of 6% for offline signatures. A FAR of 2% and an FRR of 3% were obtained for online signatures

Online signature verification using bagged svm classifier

Abstract: Data Mining is the use of algorithms to extract the information and patterns derived by the knowledge discovery in databases process Classification maps data into predefined groups or classes It is often referred to as supervised learning because the classes are determined before examining the data The Verification of handwritten Signature, which is a behavioral biometric, can be classified into off-line and online signature verification methods The feasibility and the benefits of the proposed approach are demonstrated by means of data mining problem: online Signature Verification This paper addresses using ensemble approach of Support Vector Machine for online Signature Verification Online signature verification, in general, gives a higher verification rate than off-line verification methods, because of its use of both static and dynamic features of problem space in contrast to off-line which uses only the static features We show that proposed ensemble of Support Vector Machine is superior to individual approach for Signature Verification in terms of classification rate

An improved model and feature set for signature recognition

Abstract: Automatic on-line signature recognition has been investigated by several authors in order to allow machines to recognize an user from its own biometric traits. The following paper deals with features and models required in order to allow a machine to learn and discriminate signatures. The proposed solution approaches the signature making process as the motion of a point in a bi-dimensional space and model s statistic properties of the motion via the well known Maximum a Posteriori training of Gaussian Mixture Models. Comparing our approach to state-of-the-art solutions, major advancements have been found. As first, both system accuracy in signature discrimination and system resistance to forgeries have been double. Eventually, the proposed modeling technique leads to smaller templates, whose size is halved with respect to state-of-theart alternatives.

Signature recognition-based private parking lot system

Abstract: The invention discloses a signature recognition-based private parking lot system According to a main technical scheme, when a vehicle needs to stop in a private parking lot, a vehicle owner writes the name on a touch signature board; signature recognition equipment scans the name of the vehicle owner and then processes and recognizes information of the signature as well as compares with a signature image pre-stored in the system; if the signature image is one of the pre-stored personnel signature images, a rotary motor of the parking lot is controlled to open the parking lot, so that the vehicle can be parked in the parking lot; when the vehicle leaves the parking lot, another circuit system continuously controls the rotary motor to lock the parking lot; when the vehicle is close the next time, the scanning and recognizing flow is continuously repeated

Face and Iris Fusion Recognition Based on Adaboost

Abstract: The recognition accuracy of the traditional biometric secure system is often influenced by the environment and physiological,and it leads to the development of the multimodal biometric systems.This paper presents a multi-modal biometric recognition system based on face and iris feature level fusion.Face and iris texture feature extraction is adopted Center-Symmetric Local Binary Pattern(CS-LBP) operators,the feature vectors that are extracted from face and iris are integrated linearly to form a mixed feature vector,and then Adaboost algorithm is used to select and aggregate the effective features from the mixed feature vector to build the strong classifier.Experimental results show that the system has better robustness than the mono-modal system.