PARMAK İZİ TANIMA İÇİN FARKLI SINIFLANDIRICILARIN KARŞILAŞTIRMALI BAŞARIM ANALİZİ

Year 2018, , 504 - 513, 20.07.2018

Alper Baştürk , Nurcan Sarıkaya Baştürk Orxan Qurbanov

https://doi.org/10.28948/ngumuh.443160

Cited By: 2

Abstract

Bu
çalışmada, güncel sınıflandırıcılar ve ayrıca literatürdeki mevcut bazı önemli ve
yaygın sınıflandırıcılar kullanılarak parmak izi görüntüleri tanınmıştır. Çalışmada
kullanılan sınıflandırma yöntemleri; destek vektör makineleri, k-en yakın
komşu, Naive-Bayes, karar ağacı öğrenimi ve derin sinir ağlarıdır. Eğitim ve
test veri setleri temel olarak 165 farklı parmağın dört farklı parmak izi
görüntüsü alınarak elde edilmiştir. Her bir farklı parmak izi görüntüsüne ek
olarak, bu izlerin yedi farklı döndürülmüş versiyonu da veri kümesini
genişletmek amacıyla kullanılmıştır. Her parmak izi görüntüsünün özellik
vektörü (parmak kodu), yönlü Gabor süzgeci ile süzgeçleme sonrası çıktı
görüntülerindeki ilgilenilen (sektör) alanlarının ortalaması alınarak
üretilmiştir. Parmak izi veri seti oluşturulduktan sonra, tüm sınıflandırıcılar
parmak izi görüntülerini tanımak üzere eğitilmiştir. Detaylı simülasyon çalışmaları,
parmak izi görüntülerinin tanınması amacıyla kullanılan sınıflandırıcılar
arasında en başarımlı olanının derin sinir ağı tabanlı sınıflandırıcı olduğunu
göstermiştir.

Keywords

Derin sinir ağı, parmak izi tanıma, sınıflandırma

A COMPARATIVE PERFORMANCE ANALYSIS OF VARIOUS CLASSIFIERS FOR FINGERPRINT RECOGNITION

Abstract

In
this study, recognition of fingerprint images has been performed by recent
classifiers as well as some important and common classifiers available in the
literature. The classification methods used in the study are support vector
machines, k-nearest neighbors, Naive-Bayes, decision tree learning, and deep
neural networks. Training/testing data set has been obtained basically by using
four different versions of fingerprint images of 165 different fingers.
Additional seven rotated versions of each different fingerprint images are also
used to extend the data set. Feature vector of each fingerprint image (a
fingercode) has been produced by using directional Gabor filters and averaging
specific regions (sectors) of their output images. After creating fingercode
data set, all classifiers has been trained to recognize fingerprint images. Detailed
simulation results show that deep neural networks can be effectively used among
all classifiers for recognition of fingerprint images.

Keywords

Deep neural networks, fingerprint recognition, classification

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