No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Handwritten Signature Recognition based on the SDAPCI-MS Imaging Technique
Abstract
To distinguish the handwritten signature recognition effectively, a new handwritten signature recognition method based on SDPCI-MS imaging technique is presented. The theory of SDAPCI-MS imaging technique was analyzed on the basis of analyzing the characteristic of the handwritten signature's appearance and stroke. The procedure was researched the Similarity Algorithm which used to distinguish the handwritten signature. To study the possibility and the feasibility of such a procedure, the signature recognition experiment is carried out. Results of the experimentation indicate that this method can be applied in discriminating the handwritten signature which looked like the same.
This paper proposes a new intelligent off-line Arabic handwritten signature identification and verification system based on texture analysis. The system uses the texture as feature and back propagation neural network as classifier. The signature image is preprocessed by several operations (Noise removal, Conversion of the signature image to binary image, Finding outer rectangle, Thinning and Size normalization) then the fractal number and co-occurrence matrix are computed to estimate texture features. In this work, two off-line Arabic handwritten signature identification systems are constructed. The first one uses the nearest Euclidean distance, while the other uses back propagation neural network. The paper analyzes and compares the results obtained from the two proposed systems to show the robustness level of the proposed intelligence system. Furthermore, the proposed system was tested by using Genuine signatures and has achieved a CCR (Correct Classification Rate) of 100% in best cases, while it was tested by using Forged signatures it has achieved a CRR approximated to 96.3% in best cases. The experimental results showed that the proposed system is efficient and competent with other state-of-the-art texture-based off-line signature identification systems.
In this paper, a novel method for off-line handwritten signature verification is presented. The method relies on ink concentration of a handwritten signature. The ink concentration is obtained by combining surface desorption atmospheric pressure chemical ionization mass spectrometry (SDAPCI-MS) and the Similarity Algorithm. The image of the signature is obtained by analyzing the ions of the mass spectra and extracts the relative more ink concentration as the feature. Firstly, the Euclidean distance is calculated among the handwritten signatures, but the result is not satisfied. At the end, using Similarity Algorithm, signatures is classified as authentic or forged one. The results demonstrate that this method is simple and rapid for verification of handwritten signatures.
This paper proposes an online signature authentication based on polynomial modeling. The derived feature acceleration is used. The order of the polynomial is carefully selected for each subject using the acceleration vector. The dynamic warping algorithm and mean squared error measure aid in standard template selection process for a subject. The best fit order is directly dependent on the cursive information of the signature sample. We provide a practical analysis of residual term, number of zeros inside the unit circle and distance measure on pole values for the purpose of classification. Storing order and standard template for a subject, reduces the runtime and memory requirements for a dynamic system. The best performance was achieved based on the number of zeros inside unit circle with false rejection rate as 5% and false acceptance rate as 7%. The aim of the work is to provide simple robust authentication suitable for hand held devices. Even compared to state-of art experiments, the proposed system is aimed to provide authentication using a single derived feature.
A novel recognition approach for deformed isometric surfaces is presented in this paper. Firstly, the Signature Matrix(SM) is constructed by computing the geodesic distance of every point pair using Fast Marching Method on Triangulate Domains (FMTD). Secondly, a normalization procedure is utilized to eliminate the difference of the Signature Matrices for the same object. Finally, a new series of moment invariants is proposed based on the Normalized Signature Matrix (NSM). In comparison with the recent methods, experiments indicate a lower computation complexity to the recognition for deformed isometric surfaces without reducing the recognition rate. In addition, extensions of this approach are introduced to the recognition for occluded and arbitrarily topologically deformed surfaces.
Group signature is an important anonymous technique. Two primitive properties of group signature is anonymity and unforgeability. It allows a group member to sign messages anonymously on behalf of the group such that any one can verify the signature but no one (except group manager) can find out which group member produce it. However, group manager can reveal the identity of the originator of a signature in case of a dispute. Recently Elaalim et.al proposed a short ID-based group signature, and claimed that the scheme is secure against forgeability attack and linkability attack. Unfortunately, in the paper, we show that the scheme is insecure, it exists universal forgeability, namely, anyone can forge a group signature on arbitrary message; and the scheme is linkable, namely, given two different group signatures, anyone can distinguish whether they are produced by the signer. Finally, the corresponding attacks are mounted, and the reason to such attacks are analyzed.
This paper presents a new approach of texture image classification based on nonsubsampled contourlet transform, Local binary patterns and Support vector machines. Nonsubsampled contourlet transform and Local binary patterns are used to extract texture features of images, Support vector machines are used to classify texture images. Nonsubsampled contourlet transform has translation invariability. Local Binary Patterns has rotational and gray invariance. Support vector machines have good performance in a variety of pattern recognition problems. Experimental results demonstrate that the proposed method performs much better than some existing methods. It achieves higher classification accuracy.