Abstract
The human signature is an important biometric feature that is used to identify human identity. It is essential in preventing falsification of documents in numerous financial, legal, and other commercial settings. The computerized system enters many aspects of our life, security is one of them, continues developing in computer vision and artificial network leads researcher to develop computerized signature recognition. This paper proposed a real-time algorithm for signature recognition. It is based on client and server operation.in which, client agent captures a signature and sends it to the server through the network. The server receives data and performs processing on it. Processing algorithm is based on weightless neural network. It is chosen for its simplicity and few numbers of sample required for training. The algorithm is tested and evaluated and show the ability to process 4.7 images per second.
Highlights
Technology development recently has contributed to digital devices to the escalating access and storage of confidential information
Dynamic recognition verification based on biometric information that captures during human signature. like tracking human hand movement, the position of the pen, degree of pen slope, and etc. of the feature
Static recognition verification based on biometric information that captures after the human complete signature
Summary
Those saying recognition assumes an import part of our daily life. It may be an essential property of human beings; The point when an individual sees an object, he alternately she gathers a data about the object like shape, color, size, features, and etc. all the data analyzed by mind searching for previous knowledge about the objects. if a proper match found the object recognizes. RAMs index decoder input connects to the input pattern base on biunivocal pseudo-random mapping.in the beginning, all the RAMs fill by logic ‘0’.For every training input pattern, the pointed locations in RAMs will change the contain from ‘0’ to ‘1’.this operation performed to all training pattern to create a discriminator that has memory with special combination of ‘0’ and ‘1’.the operation is repeated to all discriminator in the network but by a different training input class. Response equal to M this means identical to the class.so, r value measures the similarity between the input pattern and the learned pattern in the network compare the value for r in each discriminator and pick the maximum of them. This paper used four types of features that have been improved for their simplicity and efficiency in recognition [18]
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