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.
Convolutional Neural Network Deep Learning Face Recognition Fine Tuning Softmax
Birincil Dil | İngilizce |
---|---|
Konular | Mühendislik |
Bölüm | Review Articles |
Yazarlar | |
Yayımlanma Tarihi | 1 Aralık 2019 |
Gönderilme Tarihi | 20 Şubat 2019 |
Kabul Tarihi | 12 Haziran 2019 |
Yayımlandığı Sayı | Yıl 2019 |
Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.