Finger and signature recognition system in matlab

MATLABSolutions discover The Least Developed Technique For FingerPrint and Signature Recognition,Based On The Matching Between The Euclidean Distance And Filter Gabor Fingerprint Identification is a widely used Biometric signature. With gained interest of the research community and stakeholders towards the Finger and signature recognition, a number of solutions and algorithms have been developed and proposed to address issues related to Finger and signature operations and functions. These technological problem solutions need to be tested and validated before implementation using software simulators.

Thus we developed a general Finger and signature recognition simulation model in the MATLAB/Simulink environment, which integrates energy storage technology, load monitoring, and control capability. To show the validation of effectiveness of our simulation model, we created simulation scenarios and performed simulations using a real-world data set.

Abstract

Handwritten signatures are considered as the most natural method of authenticating a person’s identity (compared to other biometric and cryptographic forms of authentication). The learning process inherent in Neural Networks (NN) can be applied to the process of verifying handwritten signatures that are electronically captured via a stylus. This paper presents a method for verifying handwritten signatures by using a NN architecture. Various static (e.g., height, slant, etc.) and dynamic (e.g., velocity, pen tip pressure, etc.) signature features are extracted and used to train the NN. Several Network topologies are tested and their accuracy is compared. The resulting system performs reasonably well with an overall error rate of 3.3% being reported for the best case.

Discover The Least Developed Technique For Finger and signature Recognition

Finger Identification is a widely used Biometric Identification mechanism. Up till now different techniques have been proposed for having satisfactory Fingerprint Identification The widely used minutiae-based representation did not utilize a significant component of the rich discriminatory information available in the fingerprints. Local ridge structures could not be completely characterized by minutiae. The proposed filter-based algorithm uses a bank of Gabor filters to capture both local and global details in a fingerprint as a compact fixed length Finger Code. The Fingerprint Identification is based on the Euclidean distance between the two corresponding Finger Codes and hence is extremely fast and accurate than the minutiae based one. Accuracy of the system is 98.22%