Error control systems for digital communication and storage
Error control systems for digital communication and storage
Document image analysis
The recursive deterministic perceptron neural network
Neural Networks
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Biometrics, Personal Identification in Networked Society: Personal Identification in Networked Society
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Personal verification using palmprint and hand geometry biometric
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
IEEE Transactions on Image Processing
Pattern Recognition
Biometric dispersion matcher versus LDA
Pattern Recognition
Blind authentication: a secure crypto-biometric verification protocol
IEEE Transactions on Information Forensics and Security
The location method of the main hand-shape feature points
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
Expert Systems with Applications: An International Journal
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Biometric based systems for individual authentication are increasingly becoming indispensable for protecting life and property. They provide ways for uniquely and reliably authenticating people, and are difficult to counterfeit. Biometric based authenticity systems are currently used in governmental, commercial and public sectors. However, these systems can be expensive to put in place and often impose physical constraint to the users. This paper introduces an inexpensive, powerful and easy to use hand geometry based biometric person authentication system using neural networks. The proposed approach followed to construct this system consists of an acquisition device, a pre-processing stage, and a neural network based classifier. One of the novelties of this work comprises on the introduction of hand geometry's related, position independent, feature extraction and identification which can be useful in problems related to image processing and pattern recognition. Another novelty of this research comprises on the use of error correction codes to enhance the level of performance of the neural network model. A dataset made of scanned images of the right hand of fifty different people was created for this study. Identification rates and Detection Cost Function (DCF) values obtained with the system were evaluated. Several strategies for coding the outputs of the neural networks were studied. Experimental results show that, when using Error Correction Output Codes (ECOC), up to 100% identification rates and 0% DCF can be obtained. For comparison purposes, results are also given for the Support Vector Machine method.