2,065 research outputs found
Offline signature verification using classifier combination of HOG and LBP features
We present an offline signature verification system based on a signatureâs local histogram features. The signature is divided into zones using both the Cartesian and polar coordinate systems and two different histogram features are
calculated for each zone: histogram of oriented gradients (HOG) and histogram of local binary patterns (LBP). The classification is performed using Support Vector Machines (SVMs), where two different approaches for training are investigated, namely global and user-dependent SVMs. User-dependent SVMs, trained separately for each user, learn to differentiate a userâs signature from others, whereas a single global SVM trained with difference vectors
of query and reference signaturesâ features of all users, learns how to weight dissimilarities. The global SVM classifier is trained using genuine and forgery signatures of subjects that are excluded from the test set, while userdependent
SVMs are separately trained for each subject using genuine and random forgeries.
The fusion of all classifiers (global and user-dependent classifiers trained with each feature type), achieves a 15.41% equal error rate in skilled forgery test, in the GPDS-160 signature database without using any skilled forgeries
in training
Online Signature Verification: Present State of Technology
The way a person signs his or her name is known to be characteristic of that individual. Signatures are influenced by the physical and emotional conditions of a subject. A signature verification system must be able to detect forgeries, and, at the same time, reduce rejection of genuine signatures. Significant research has been conducted in feature extraction and selection for the application of on-line signature verification. All these features may be important for some problems, but for a given task, only a small subset of features is relevant. In addition to a reduction in storage requirements and computational cost, these may also lead to an improvement in general performance. On the other hand, selection of a feature subset requires a multi-criterion optimization function, e.g. the number of features and accuracy of classification. In this paper all these techniques are reviewed
A hybrid HMM/ANN based approach for online signature verification
2007-2008 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe
Off-line Signature Verification Based on Fusion of Grid and Global Features Using Neural Networks
Signature is widely used and developed area of research for personal verification and authentication. In this paper Off-line Signature Verification Based on Fusion of Grid and Global Features Using Neural Networks (SVFGNN) is presented. The global and grid features are fused to generate set of features for the verification of signature. The test signature is compared with data base signatures based on the set of features and match/non match of signatures is decided with the help of Neural Network. The performance analysis is conducted on random, unskilled and skilled signature forgeries along with genuine signatures. It is observed that FAR and FRR results are improved in the proposed method compared to the existing algorithm
Signature Recognition System for Student Attendance System in UTP
This paper proposes an off-line signature recognition system for student attendance
system in Universiti Teknologi PETRONAS (UTP). In current system, attendance sheet
is passed across the class and students are required to signed on the paper. Later,
lecturers will check on the paper and mark any empty column. However, lecturers
always busy and seldom have time to check each signature. Basically, the system has the
ability to imitate humans' capability of recognizing signatures. Thus, it could help
lecturers in recognizing students' signatures. The system employs artificial neural
networks for recognition and training process. This system is developed mainly using
Visual Basic 6.0 and involves four basic steps, which are image acquisition, image pre
processing, and enrolment and verification process. It has two phases, training and
recognition. Both process use artificial neural network. The system was satisfactory in
all cases where there were two different signatures to be recognized with False Rejection
Rate (FRR) for genuine signature is 4% and False Acceptance Rate (FAR) for forged
signature is 28%
Introduction to Presentation Attacks in Signature Biometrics and Recent Advances
Applications based on biometric authentication have received a lot of
interest in the last years due to the breathtaking results obtained using
personal traits such as face or fingerprint. However, it is important not to
forget that these biometric systems have to withstand different types of
possible attacks. This chapter carries out an analysis of different
Presentation Attack (PA) scenarios for on-line handwritten signature
verification. The main contributions of this chapter are: i) an updated
overview of representative methods for Presentation Attack Detection (PAD) in
signature biometrics; ii) a description of the different levels of PAs existing
in on-line signature verification regarding the amount of information available
to the impostor, as well as the training, effort, and ability to perform the
forgeries; and iii) an evaluation of the system performance in signature
biometrics under different scenarios considering recent publicly available
signature databases, DeepSignDB and SVC2021_EvalDB. This work is in line with
recent efforts in the Common Criteria standardization community towards
security evaluation of biometric systems.Comment: Chapter of the Handbook of Biometric Anti-Spoofing (Third Edition
Drawing, Handwriting Processing Analysis: New Advances and Challenges
International audienceDrawing and handwriting are communicational skills that are fundamental in geopolitical, ideological and technological evolutions of all time. drawingand handwriting are still useful in defining innovative applications in numerous fields. In this regard, researchers have to solve new problems like those related to the manner in which drawing and handwriting become an efficient way to command various connected objects; or to validate graphomotor skills as evident and objective sources of data useful in the study of human beings, their capabilities and their limits from birth to decline
Online signature verification using hybrid wavelet transform
Online signature verification is a prominent behavioral biometric trait. It offers many dynamic features along with static two dimensional signature image. In this paper, the Hybrid Wavelet Transform (HWT) was generated using Kronecker product of two orthogonal transform such as DCT, DHT, Haar, Hadamard and Kekre. HWT has the ability to analyze the signal at global as well as local level like wavelet transform. HWT-1 and -2 was applied on the first 128 samples of the pressure parameter and first 16 samples of the output were used as feature vector for signature verification. This feature vector is given to Left to Right HMM classifier to identify the genuine and forged signature. For HWT-1, DCT HAAR offers best FAR and FRR. . For HWT-2, KEKRE 128 offers best FAR and FRR. HWT-1 offers better performance than HWT- 2 in terms of FAR and FRR. As the number of states increase, the performance of the system improves. For HWT - 1, KEKRE 128 offers best performance at 275 symbols whereas for HWT - 2, best performance is at 475 symbols by KEKRE 128
Efficient Signatures Verification System Based on Artificial Neural Networks
Biometrics refer to the system of authenticating identities of humans, using features like retina scans, thumb and fingerprint scanning, face recognition and also signature recognition. Signatures are a simple and natural method of verifying a personâs identity. It can be saved as an image and verified by matching, using neural networks. Signature verification can be offline or online. In this work, we present a system for offline signature verification. The user has to submit a number of signatures that are used to extract two types of features, statistical features and structural features. A vector obtained from each of them is used to train propagation neural network in the verification stage. A test signature is then taken from the user, to compare it with those the network had been trained with. A test experiment was carried out with two sets of data. One set is used as a training set for the propagation neural network in its verification stage. This set with four signatures form each user is used for the training purpose. The second set consists of one sample of signature for each of the 20 persons is used as a test set for the system. A negative identification test was carried out using a signature of one person to test othersâ signatures. The experimental results for the accuracy showed excellent false reject rate and false acceptance rate
- âŠ