Automatic Handwritten Signature Verification Research Articles

Multi-dilation Convolutional Neural Network for Automatic Handwritten Signature Verification

Multi-dilation Convolutional Neural Network for Automatic Handwritten Signature Verification

Smartphone-based Handwritten Signature Verification using Acoustic Signals

Handwritten signature verification techniques, which can facilitate user authentication and enable secure information exchange, are still important in property safety. However, on-line automatic handwritten signature verification usually requires dynamic handwritten patterns captured by a special device, such as a sensor-instrumented pen, a tablet or a smartwatch on the dominant hand. This paper presents SonarSign, an on-line handwritten signature verification system based on inaudible acoustic signals. The key insight is to use acoustic signals to capture the dynamic handwritten signature patterns for verification. Particularly, SonarSign exploits the built-in speakers and microphones of smartphones to transmit a specially designed training sequence and record the corresponding echo for channel impulse response (CIR) estimation, respectively. Based on the sensitivity of CIR to the tiny surrounding environment changes including handwritten signature actions, SonarSign designs an attentional multi-modal Siamese network for end-to-end signatures verification. First, multi-modal CIR streams are fused to extract representative signature pattern features from spatio-temporal dimensions. Then an attentional Siamese network is elaborated to verify whether the given two signatures are from the same signatory. Extensive experiments in real-world scenarios show that SonarSign can achieve accurate and robust signatures verification with an AUC (Area Under ROC (Receiver Operating Characteristic) Curve) of 98.02% and an EER (Equal Error Rate) of 5.79% for unseen users.

Writer-independent signature verification based on feature extraction fusion

In this work, with the aim of separating the genuine and forgery samples of the signature, we developed an automatic handwritten signature verification (AH-SV) based on the informative feature. The large intra-class variation is an important challenge among specific writer samples in SV. Thus, the proposed method is focused on feature extraction fusion (FEF) to increase the accuracy and efficiency of the system. We use features such as extracting intersection points based on neighboring intersection pixels relative to the central pixel to achieve the most informative feature, which can be regarded as a model for estimating the transitional probabilities of the signature strokes. Discriminative features extraction for SV relies on the domain of graphology and texture description. Therefore, we extracted parametric features and local binary pattern (LBP) from the signature images, and then we applied fusion using canonical correlation analysis (CCA) to improve the discriminative features. Hence, the measurement of the proximity between genuine and forgery signatures can be computed by their corresponding feature vectors. We used MCYT, GPDSsynthetic and CEDAR datasets, with the classification of writer-independent structure on k-nearest neighbour (K-NN) classifier. Moreover, in the experiment phase, we selected random number of samples in datasets. The experimental results show that the accuracy in the CEDAR dataset has achieved the best value. Also, the results of the average error rate, false acceptance rate, and false rejection rate criteria have improved compared to well-known methods.

A different approach to off-line handwritten signature verification using the optimal dynamic time warping algorithm

In this paper, a method for the automatic handwritten signature verification (AHSV) is described. The method relies on global features that summarize different aspects of signature shape and dynami...

A different approach to off-line handwritten signature verification using the optimal dynamic time warping algorithm

In this paper, a method for the automatic handwritten signature verification (AHSV) is described. The method relies on global features that summarize different aspects of signature shape and dynamics of signature production. For designing the algorithm, we have tried to detect the signature without paying any attention to the thickness and size of it. The results have shown that the correctness of our algorithm detecting the signature is more acceptable. In this method, first the signature is pre-processed and the noise of sample signature is removed. Then, the signature is analyzed and specification of it is extracted and saved in a string for the comparison. At the end, using adapted version of the dynamic time warping algorithm, signature is classified as an original or a forgery one.

A neural network approach to off-line signature verification using directional PDF

A neural network approach is proposed to build the first stage of an Automatic Handwritten Signature Verification System. The directional Probability Density Function was used as a global shape factor and its discriminating power was enhanced by reducing its cardinality via filtering. Various experimental protocols were used to implement the backpropagation network (BPN) classifier. A comparison, on the same database and with the same decision rule, shows that the BPN classifier is clearly better than the threshold classifier and compares favourably with the k-Nearest-Neighbour classifier.

STRUCTURAL INTERPRETATION OF HANDWRITTEN SIGNATURE IMAGES

The interpretation of handwritten signature images should be closely related to the writer’s identity. The representation and analysis of the handwritten signature is the major challenge in the field of automatic signature verification. A new concept of representation and interpretation of handwritten signature images is advocated. The segmentation process breaks up the signature into a collection of arbitrarily-shaped primitives. In the next step, a local interpretation process serves as a sophisticated template matching, permitting the labeling of all primitives from the test primitive set. This is followed by the global interpretation process, which permits the evaluation of a similarity measure between two structural graphs. Experimental results obtained from a database of 800 handwritten signature images from 20 writers show a performance with a type I error rate of ∈1=1.50%, a type II error rate of ∈2=1.37% and a total error rate ∈t=1.43% in the best strategy proposed using a minimum-distance classifier and two reference signatures. A complete description of this novel automatic handwritten signature verification system is presented in this paper.

AN EVALUATION OF PARALLEL STRATEGIES FOR FEATURE VECTOR CONSTRUCTION IN AUTOMATIC SIGNATURE VERIFICATION SYSTEMS

This paper describes possible strategies for the implementation of a feature selection algorithm particularly suited to the realisation of an efficient automatic handwritten signature verification system in which an active feature vector, optimised with respect to an individual signer, is constructed during an enrollment period. A range of configurations based on transputer arrays are considered and the possible implementational approaches evaluated. The paper demonstrates how the inherent parallelism which exists within a generic model for verification can be exploited to provide an optimised general-purpose framework for verification processing.