A MULTIMODAL COMBINED MACHINE LEARNING APPROACH FOR FINGERPRINT CLASSIFICATION

Authors

  • A.Thilagavathy, Ravin N Krishnan, C Sidhartha Reddy, Sode Bharath Chandra

Abstract

Fingerprint identification is the most widely used biometric for a multitude of security applications ranging from phone unlocks to bank security. All modern systems use a machine learning approach based on a unique algorithmic - such as Support Vector Machines(SVM) , ConvolutionalĀ  Neural Networks(CNN) or Residual Convolutional Neural Network(RESCNN).Each and every algorithm is strong in some areas and weak in others. In our work we describe the output yielded using a new proposed algorithm that uses a trifecta combination, i.e three different algorithms are combined to maximise their individual strengths and cover each other's weaknesses. The algorithms we combine are a simple preprocessing algorithm, SVM and a trained convolutional neural network. First, the preprocessing algorithms smoothens, sharpens and filters the image, then an SVM is used to extract the minutiae (fingerprint features) which is finally classified using a trained CNN classifier. This new algorithmic approach will have enhanced accuracy, faster processing time and lower error than the traditional unilateral algorithmic approaches.

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Published

2021-11-27

How to Cite

A.Thilagavathy, Ravin N Krishnan, C Sidhartha Reddy, Sode Bharath Chandra. (2021). A MULTIMODAL COMBINED MACHINE LEARNING APPROACH FOR FINGERPRINT CLASSIFICATION. International Journal of Modern Agriculture, 10(3), 151 - 156. Retrieved from http://modern-journals.com/index.php/ijma/article/view/1525

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