A Multimodal Combined Machine Learning Approach for Fingerprint Classification

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) , Convolutional  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.