TY - JOUR AU - A.Thilagavathy, Ravin N Krishnan, C Sidhartha Reddy, Sode Bharath Chandra, PY - 2021/11/27 Y2 - 2024/03/28 TI - A MULTIMODAL COMBINED MACHINE LEARNING APPROACH FOR FINGERPRINT CLASSIFICATION JF - International Journal of Modern Agriculture JA - IJMA VL - 10 IS - 3 SE - DO - UR - http://www.modern-journals.com/index.php/ijma/article/view/1525 SP - 151 - 156 AB - <p>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&nbsp; 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.</p> ER -