Off-line Handwritten Signature Verification Research Articles

Handwritten signature verification using the quad-tree histogram of templates and a Support Vector-based artificial immune classification

This work proposes a novel system for off-line handwritten signature verification. A new descriptor founded on a quad-tree structure of the Histogram Of Templates (HOT) is introduced. For the verification step, we propose a robust implementation of the Artificial Immune Recognition System (AIRS). This classifier is inspired from the natural immune system, which generates antibodies to protect the human body against antigens. The AIRS training develops new memory cells that are subsequently used to recognize data through a k Nearest Neighbor (kNN) classification. Presently, to get a robust verification, the kNN classification is substituted by a Support Vector (SV) decision, yielding the AIRSV classifier. Experiments are performed on three datasets, namely, MCYT-75, GPDS-300 and GPDS-4000. AIRSV performance is assessed comparatively to both conventional AIRS as well as SVM. Obtained results demonstrated that AIRSV is more effective than classical AIRS. Moreover, the proposed signature verification system gives similar and sometimes better performance than SVM as well as the state-of-the-art methods.

Static Signature Verification Based on Texture Analysis Using Support Vector Machine

Off-line hand written signature verification performs at the global level of image. It processes the gray level information in the image using statistical texture features. The textures and co-occurrence matrix are analyzed for features extraction. A first order histogram is also processed to reduce different writing ink pens used by signers. Samples of signature are trained with SVM model where random and skilled forgeries have been used for testing. Experimental results are performed on two databases: MCYT-75 and GPDS Synthetic Signature Corpus.

New off-line Handwritten Signature Verification method based on Artificial Immune Recognition System

Natural Immune System offers many interesting features that inspired the design of Artificial Immune Systems (AIS) used to solve various problems of engineering and artificial intelligence. AIS are particularly successful in fault detection and diagnosis applications where anomalies such as errors and failures are assimilated to viruses that should be detected. Thereby, AIS seem suitable to automatically detect forgeries in signature verification systems. This paper proposes a novel method for off-line signature verification that is based on the Artificial Immune Recognition System (AIRS). For feature generation, two different descriptors are proposed to generate signature traits. The first is the Gradient Local Binary Patterns that estimates gradient features based on the LBP neighborhood. The second descriptor is the Longest Run Feature, which describes the signature topology by considering longest suites of text pixels. Performance evaluation is carried out on CEDAR and GPDS-100 datasets. The results obtained showed that the proposed system has promising performance and often comfortably outperforms the state of the art.

Offline handwritten signature verification using local and global features

This paper presents a new approach to address the problem of offline handwritten signature verification. In contrast to many existing systems, we are interested in making soft decision rather than a purely binary classification for the signatures under verification. To accomplish this goal, we incorporate both types of features: finer intensity-based features and global geometry-based features. Particularly, the finer features are computed for every sample point of a signature using histogram of intensities, and the geometry-based features are extracted using an adaptation of the shape context descriptor. One of the advantages of our approach is that the extracted features are very robust to noise, rotation and scaling change without heavily relying on any complicated pre-processing steps. The extracted features are used to compute the similarity score, followed by a score calibration process to estimate the corresponding confidence score (i.e., using log-likelihood-ratio). To validate our work, we submitted our system to the signature verification competition (SignComp2011) and achieved quite good results.

Discriminative Features Mining for Offline Handwritten Signature Verification

Signature verification is an active research area in the field of pattern recognition. It is employed to identify the particular person with the help of his/her signature’s characteristics such as pen pressure, loops shape, speed of writing and up down motion of pen, writing speed, pen pressure, shape of loops, etc. in order to identify that person. However, in the entire process, features extraction and selection stage is of prime importance. Since several signatures have similar strokes, characteristics and sizes. Accordingly, this paper presents combination of orientation of the skeleton and gravity centre point to extract accurate pattern features of signature data in offline signature verification system. Promising results have proved the success of the integration of the two methods.

Off-line handwritten signature detection by analysis of evidence accumulation

One fundamental step in off-line handwritten signature verification is the detection of the signature position within the document image. This paper introduces an original approach for signature position detection. The method is based on an accumulative evidence technique, searching the region that maximizes some measure of correspondence with a given reference signature. This measure is based on the similarity of the slope marked out by each of the strokes in the signature. Experiments have shown that the method can be used on real documents, such as bank checks, where images have a high noise level due to background interferences (i.e. machine or handwritten texts, stamps, and lines). The proposed method is robust to variability in the size of the signatures and has the advantage of using only one reference signature per person.

Off-line signature verification based on grey level information using texture features

A method for conducting off-line handwritten signature verification is described. It works at the global image level and measures the grey level variations in the image using statistical texture features. The co-occurrence matrix and local binary pattern are analysed and used as features. This method begins with a proposed background removal. A histogram is also processed to reduce the influence of different writing ink pens used by signers. Genuine samples and random forgeries have been used to train an SVM model and random and skilled forgeries have been used for testing it. Results are reasonable according to the state-of-the-art and approaches that use the same two databases: MCYT-75 and GPDS-100 Corpuses. The combination of the proposed features and those proposed by other authors, based on geometric information, also promises improvements in performance.

A Preliminary Study on Various Off-line Hand Written Signature Verification Approaches

Biometrics can be classified into two broad categories— behavioral (signature verification, keystroke dynamics, etc.) and physiological (iris characteristics, fingerprint, etc.). Handwritten signature is amongst the first few biometrics to be used even before the advent of computers. Signature verification is widely studied and discussed using two approaches [5]. On-line approach uses an electronic tablet and a stylus connected to a computer to extract information about a signature and takes dynamic information like; pressure, velocity, etc whereas in offline approach stable dynamic variations are not used for verification purpose. Offline systems are more applicable and easy to use in comparison with on-line systems in many parts of the world however it is considered more difficult than on-line verification due to the lack of dynamic information. The paper presents a survey of off-line signature verification approaches being followed in different areas. This being a nascent area under research, the survey covers some of the examples of the ways

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.

Statistical Approach for Offline Handwritten Signature Verification

Signatures were considered an important tool for authenticating the identity of human beings. So, signature verification was one of the biggest uses for that. We proposed an algorithmic approach for the verification of handwritten signatures by applying some statistical methods. The research work was based on the collection of set of signatures from which an average signature was obtained based on our algorithm and then taking decision of acceptance after analyzing the correlation in between the sample signature and the average signature.

A hybrid opto-electronic method for fast off-line handwritten signature verification

This paper proposes a hybrid opto-electronic method for the fast automatic verification of handwritten signatures. This method combines several statistical classifiers and consists of three steps. The first step aims to transform the original signatures using the identity and four Gabor transforms. For each image transform, the second step is to intercorrelate the analysed signature with the similarly transformed signatures of the learning database. Finally, the third step performs the verification of the authenticity of signatures by fusing the decisions related to each transform. Image transforms and intercorrelations can be computed in real time using a high-speed optical correlator. The different decisions and their fusion are then digitally performed. The opto-electronic implementation of the proposed method has been simulated on a large database, taking into account the specific constraints of the optical implementation. Satisfactory results have been obtained. Indeed, the proposed system allows the rejection of 62.4% of the forgeries used for the experiments when 99% of genuine signatures are correctly recognized.

Wavelet-Based Off-Line Handwritten Signature Verification

In this paper, a wavelet-based off-line handwritten signature verification system is proposed. The proposed system can automatically identify useful and common features which consistently exist within different signatures of the same person and, based on these features, verify whether a signature is a forgery or not. The system starts with a closed-contour tracing algorithm. The curvature data of the traced closed contours are decomposed into multiresolutional signals using wavelet transforms. Then the zero-crossings corresponding to the curvature data are extracted as features for matching. Moreover, a statistical measurement is devised to decide systematically which closed contours and their associated frequency data of a writer are most stable and discriminating. Based on these data, the optimal threshold value which controls the accuracy of the feature extraction process is calculated. The proposed approach can be applied to both on-line and off-line signature verification systems. Experimental results show that the average success rates for English signatures and Chinese signatures are 92.57% and 93.68%, respectively.