Handwritten signature is broadly utilized as personal verification in financial institutions ensures the necessity for a robust automatic signature verification tool. This tool aims to reduce fraud in all related financial transactions’ sectors. This paper proposes an online, robust, and automatic signature verification technique using the recent advances in image processing and machine learning. Once the image of a handwritten signature for a customer is captured, several pre-processing steps are performed on it including filtration and detection of the signature edges. Afterwards, a feature extraction process is applied on the image to extract Speeded up Robust Features (SURF) and Scale-Invariant Feature Transform (SIFT) features. Finally, a verification process is developed and applied to compare the extracted image features with those stored in the database for the specified customer. Results indicate high accuracy, simplicity, and rapidity of the developed technique, which are the main criteria to judge a signature verification tool in banking and other financial institutions.

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Image Processing Based Signature Verification Technique to Reduce Fraud in Financial Institutions

The automatic, highly accurate and instantaneous authentication of a static off-line handwritten signature is highly required in financial, legal and governmental sectors. The genuine signature features are extracted by models like scale-invariant feature transform (SIFT) and speeded-up robust features (SURF), then the common patterns among these features are recognised for verifying new signatures. Although SIFT/SURF-based matching models are commonly used because of their robustness and low computational complexity, their tolerance to the unconstrained signature styles of large variations is not very satisfactory. This work presents three main drawbacks of the traditional application of this model in signature verification and proposes a set of modifications to customise this model to fit the nature of the handwriting domain. These modifications consider initially the limited size of the descriptor window in SIFT/SURF model, then consider the single point-to-point and the crisp matching of the p...

An enhanced handwritten signature verification model applied on off-line benchmark data sets

With the development of social informatization technology, the problems of individual information security are becoming serious. Nowadays identity identification has been required essentially in government and business field. In this paper, we summarize and analyze the identification principles and identification methods based on biometrics, including the present researches fingerprint, palmprint, iris, human face, vein, gait and signature, and make comparative analysis of the differences of the error recognition rate, stability, acquisition difficulty and counterfeiting degree. Finally, the prospects of biometric recognition technologies are discussed additionally.

A Review of Recent Advances in Identity Identification Technology Based on Biological Features.

which refers to identifying an individual based on his or her physiological or behavioral characteristics, has the capabilities to the reliably distinguish between an authorized person and an imposter. The Signature recognition systems can categorized as offline (static) and online (dynamic). This paper presents Surf Feature based recognition of offline signatures system that is trained with low-resolution scanned signature images. The signature of a person is an important biometric attribute of a human being which can be used to authenticate human identity. However the signatures of human can be handled as an image and recognized using computer vision and HMM techniques. With modern computers, there is need to develop fast algorithms for signature recognition. There are multiple techniques are defined to signature recognition with a lot of scope of research. In this paper, (static signature) off-line signature recognition & verification using surf feature with HMM is proposed, where the signature is captured and presented to the user in an image format. Signatures are verified depended on parameters extracted from the signature using various image processing techniques. The Off-line Signature Verification and Recognition is implemented using Mat lab platform. This work has been analyzed or tested and found suitable for its purpose or result. The proposed method performs better than the other recently proposed methods. KeywordsSignature verification, offline signature recognition, signatures, SURF features and HMM.

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Offline Signature Verification in Punjabi based on SURF Features and Critical Point Matching using HMM

Offline Signature Verification in Punjabi Based On SURF Features and Critical Point Matching Using HMM

Biometrics, which refers to identifying an individual based on his or her physiological or behavioral uniqueness, reliably distinguishes between an authorized person and imposter. Signature can be verified in different parts as offline (static) and online (dynamic). This paper presents HMM with surf feature based recognition of offline signatures system that is trained with low-resolution scanned signature images. The signature of a person is an important biometric attribute of a human being which can be used to authenticate human identity. However human signatures is technique that can be handled as image and recognized using computer vision or operations and hidden Markov model(HMM) with surf feature techniques. With modern computers, there is need to develop fast algorithms for signature verification and recognition. There are many different approaches discussed to signature recognition and verification for research. In this paper, off-line signature verification using HMM and surf feature is proposed, where the signature is captured and presented to the user in the form of scanned image. Signatures verification can be perform based on different parameters extracted from the signature using various image processing techniques. The Off-line (static) Signature verification and recognition is implemented using Mat lab toolbox. This work has been tested (Analyzing) and found suitable for its purpose. For the implementation of this proposed work Mat lab software is used.

Rajpal Kaur

Papers

Offline Signature Verification in Punjabi based on SURF Features and Critical Point Matching using HMM

Offline Signature Verification in Punjabi Based On SURF Features and Critical Point Matching Using HMM

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