Fingerprint classification using a deep convolutional neural network

Biometric systems detect authenticity based on users' distinct physiological or behavioral characteristics for purposes of identification and access control. These pattern recognition systems are difficult to bypass when compared to traditional token or password based systems. This paper is pro...

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Bibliographic Details
Published in2018 4th International Conference on Information Management (ICIM) pp. 86 - 91
Main Authors Pandya, Bhavesh, Cosma, Georgina, Alani, Ali A., Taherkhani, Aboozar, Bharadi, Vinayak, McGinnity, T.M
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2018
Subjects
Biometrics
CNN
Convolution Neural Network
Convolutional neural networks
Deep Learning
Feature extraction
Fingerprint Classification
Fingerprint recognition
Gabor filters
Histograms
Image matching
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Summary:Biometric systems detect authenticity based on users' distinct physiological or behavioral characteristics for purposes of identification and access control. These pattern recognition systems are difficult to bypass when compared to traditional token or password based systems. This paper is proposing a new deep learning architecture for fingerprint recognition. The proposed architecture comprises of a pre-processing stage for extracting texture features from fingerprints, and this stage is performed by using histogram equalization, Gabor enhancement and fingerprint thinning. The pre-processed fingerprints are input into a Deep Convolutional Neural Network classifier. The proposed approach has achieved 98.21% classification accuracy with 0.9 loss. The obtained accuracy is significantly higher than previously reported results on the same dataset, 77%.
DOI:10.1109/INFOMAN.2018.8392815