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

Year 2019, , 129 - 149, 01.12.2019

Fatıma Zehra Ünal

https://doi.org/10.33769/aupse.529575

Cited By: 1

Abstract

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.

Keywords

Convolutional Neural Network, Deep Learning, Face Recognition, Fine Tuning, Softmax

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