A Novel Technique for Fingerprint Classification Based on Fuzzy C-Means and Naive Bayes Classifier

Abstract

-Fingerprint classification is a key issue in automatic fingerprint identification systems. One of the main goals is to reduce the item search time within the fingerprint database without affecting the accuracy rate. In this paper, a novel technique, based on topological information, for efficient fingerprint classification is described. The proposed system is composed of two independent modules: the former module, based on Fuzzy C-Means, extracts the best set of training images; the latter module, based on Fuzzy C-Means and Naive Bayes classifier, assigns a class to each processed fingerprint using only directional image information. The proposed approach does not require any image enhancement phase. Experimental trials, conducted on a subset of the free downloadable PolyU database, show a classification rate of 91% over a 100 images test database using only 12 training examples.

Fingerprint Classification Based on Orientation Field

“…In our work, the nonlinear SVMs classifier was applied according to figure (7) in addition, parameters were chosen as C=1000, = 0.04. The performance of the proposed method is presented by conducting on two databases.…”

“…(iv) Cognitive method, for example, neural network, fuzzy and support vector machines (SVMs) were relay on feature vector either from singularity region or directional image and processed it to obtain final classification based on a pyramidal architecture [4]. different classification techniques have been proposed such as hierarchical classifier based on K Nearest Neighbors (KNN) and SVM for feature extracted from orientation field and complex filter [5] and convolutional Neural Network was used for classification [6] as well as Fuzzy C-Means (FCM) and Naive Bayes classifier demonstrated for classification fingerprint into 4 classes [7] .…”

Fitur Esktraksi LBP dan Naive Bayes dalam Klasifikasi Jenis Pepaya Berdasarkan Citra Daun

“…Sedangkan untuk klasifikasi daun menggunakan algoritma Naïve Bayes yang merupakan metode prediksi berbasis probabilitas sederhana yang berdasarkan Teorema Bayes. Metode ini memiliki asumsi yang independi yang kuat dan model fitur independen [10] [11].…”

“…Hasil tersebut menunjukkan bahwa peringkat positif yang baik dan hasil dari positif palsu yang sangat minim yaitu 0,09 % [13] [16]. Metode ini memiliki asumsi yang independi yang kuat dan model fitur independen [10] [11]. Klasifikasi Naïve Bayes adalah metode yang paling sederhana dengan menggunakan peluang yang ada, dimana tempatnya mengasumsikan bahwa setiap variabel adalah independensi [10].…”

A Novel Technique for Fingerprint Classification based on Naive Bayes Classifier and Support Vector Machine

“…All model parameters (i.e., previous class possibilities and have probability distributions) are often approximated with relative frequencies from the training set. [8] These are most probability estimates of the probabilities. Previous class possibilities are also calculated using two methods:…”