A COMPARISON OF DEEP LEARNING BASED ARCHITECTURE WITH A CONVENTIONAL APPROACH FOR FACE RECOGNITION PROBLEM

Fatıma Zehra ÜNAL

Makale detayları

A COMPARISON OF DEEP LEARNING BASED ARCHITECTURE WITH A CONVENTIONAL APPROACH FOR FACE RECOGNITION PROBLEM

Yazarlar:

Fatıma Zehra ÜNAL

Yayıncı:

Ankara University

Ana Başlıklar:

Uygulamalı fizik

Dergi:

Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 2019 Vol.61 Issue 2, pp.129-149

Belge Tipi:

Makale

Yayın Tarihi:

11/2019

Belge Dili:

İngilizce

Document ID (Org No.):

2000910978

Sıklık:

İki Yıllık

eISSN:

2618-6462

Özet:

 

This
paper addresses a new approach for face recognition problem based on deep
learning strategy. In order to verify the performance of the proposed approach,
it is compared with a conventional face recognition method by using various
comprehensive datasets.  The conventional
approach employs Histogram of Gradient (HOG) algorithm to extract features and
utilizes a multi-class Support Vector Machine (SVM) classifier to train and
learn the classification. On the other hand, the proposed deep learning based
approaches employ a Convolutional Neural Network (CNN) based architecture and
also offer both a SVM and Softmax classifiers respectively for the
classification phase. Results reveal that the proposed deep learning
architecture using Softmax classifier outperform conventional method by a
substantial margin. As well as, the deep learning architecture using Softmax
classifier also outperform SVM in almost all cases.

Makale detayları

A COMPARISON OF DEEP LEARNING BASED ARCHITECTURE WITH A CONVENTIONAL APPROACH FOR FACE RECOGNITION PROBLEM

Yazarlar: Fatıma Zehra ÜNAL

Yayıncı: Ankara University

Ana Başlıklar: Uygulamalı fizik

Dergi: Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 2019 Vol.61 Issue 2, pp.129-149

Belge Tipi: Makale

Yayın Tarihi: 11/2019

Belge Dili: İngilizce

Document ID (Org No.): 2000910978

Sıklık: İki Yıllık

eISSN: 2618-6462

Sayı

Yazarlar:

Fatıma Zehra ÜNAL

Yayıncı:

Ankara University

Ana Başlıklar:

Uygulamalı fizik

Dergi:

Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 2019 Vol.61 Issue 2, pp.129-149

Belge Tipi:

Makale

Yayın Tarihi:

11/2019

Belge Dili:

İngilizce

Document ID (Org No.):

2000910978

Sıklık:

İki Yıllık

eISSN:

2618-6462