Browsing by Author "Prabodith, N.P.C."

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    Artificial Neural Network Based Signature Recognition and Verification System
    (Uva Wellassa University of Sri Lanka, 2013) Prabodith, N.P.C.
    The signature of a person is an important biometric attribute of a human being which can be used to authenticate human identity. In present day signature use in many transactions in day today life and we can see some people are trying to miss use signature for achieve their narrow goals. So goal of this project is provide strong way system recognize and verify hand written signature. Signatures are intrapersonal biometric attribute and it differs from people to people. Even collection of signature of one person also differs from each other. But when we consider that collection of signatures, there is certain pattern which follows all signatures. So Signature recognition is such kind of pattern recognition and human signatures can be handled as an image and recognized using computer vision and neural network techniques. With modern computers, there is need to develop fast algorithms for signature recognition. There are various approaches to signature recognition with a lot of scope of research. In here, off-line signature recognition & verification using neural network is proposed, where the signature is captured and presented to the user in an image format. Signatures are verified based on parameters extracted from the signature that using various image processing techniques. This System accepted image of image and generate single output. That output can be single digit or pattern which containing binary values. Digital Image Processing and Artificial Neural Networks techniques are main techniques used for implement system. According to the system DIP and ANN are two main parts in development side. When system accepts signature by image format and then its process using DIP. System gathered some unique features (Details) form Image and which use as input in ANN. In here for both Image processing and Artificial Neural Networks programming language based on C#.net with the help of Aforge. net computer vision library. Customized classes of Aforge. net Imaging library used for image preprocessing activities while all feature extraction activities algorithms implemented by using C#.net language. Aforge Neuro library used for ANN programming. Those two parts combined together and works as complete system.
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    Artificial Neural Network based Signature Recognition and Verification System
    (Uva Wellassa University of Sri Lanka, 2013) Prabodith, N.P.C.; Wimaladharma, S.T.C.I.
    The signature is an important biometric attribute of a person which can be used to authenticate the personal identity because of its uniqueness for each person. Now a day the personal signature has a significant value in day to day works. Because of its uniqueness there is a certain pattern which can be identified by extracting certain unique features. Though in present days signatures are using as the valid authentication mechanism, some peoples are trying to imitate another person’s signature to achieve some narrow goals. It is also common sight in Sri Lanka place like banks, government organizations, universities etc. Therefore, it is essential to introduce a high accuracy validation mechanism for personal authentication. The proposed system uses an efficient image processing and feature extraction methods as well as well-trained neural network system. Methodology Implementation of the system mainly based on two phases, Training phase and recognition phase. Several technologies, Programming languages and Libraries are used to design and implement the system. In the training phase there are several steps to be completed by the system before the training get started. In pre-processing activities, system is mainly focusing on background elimination, cropping (crop signature image according to the signature bounds), thresholding, thinning, and image width transformation (Abikoye et al., 2011). Feature extraction is one of the important parts of the system because powerful features directly affect to the accuracy of the final output. In here firstly, system will collect some global features such as pixel density, width to height ratio, maximum horizontal count and maximum vertical count. Then system will extract some unique points based on vertical splitting and horizontal splitting (Ashwiniet al., 2012). Those feature points are related with the image geometric centre point. After the feature extraction process is completed and then system normalizes all the features and added them to the input vector. Main purpose of the normalization is to convert values into an acceptable range for the neural network (range between 0 and 1). Then, the normalize data are used to train the Neural Network. In the Recognition phase, all the steps up to feature normalization are accomplished and those features are compared with trained Neural Network. Finally, the system will generate a unique value and which reveals the acceptance and rejection of the relevant signature.