Comparative Analysis of Accuracy of Face Recognition System Using CNN and SVM

The aim of this work is to recognize the face images by comparing the performance of accuracy using Convolutional Neural Network (CNN) Support Vector Machine. Convolutional Neural Network and Support Vector Machine were implemented to recognize the face images and to analyze the accuracy using MATLA...

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Bibliographic Details
Published in:	2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS) pp. 1 - 5
Main Authors:	Reddy, P. Ram Kiran, Uganya, G.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 10-10-2022
Subjects:	Accuracy Face Recognition Machine Learning Novel Convolutional Neural Network Support Vector Machine
Online Access:	Get full text
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Summary:	The aim of this work is to recognize the face images by comparing the performance of accuracy using Convolutional Neural Network (CNN) Support Vector Machine. Convolutional Neural Network and Support Vector Machine were implemented to recognize the face images and to analyze the accuracy using MATLAB. Group 1 with 20 samples of CNN and group 2 with 20 samples of SVM were collected to analyze with the help of 80% of G-power. From the MATLAB simulation result, Convolutional Neural Network achieves image accuracy rate of 95% and Support Vector Machine achieves image accuracy rate of 83%. The achieved significance value was 0.01 (" \mathrm{p} < 0.05 "). Conclusion - From this analysis, Novel Convolutional Neural Network achieves significantly better accuracy compared to Support Vector Machine.
DOI:	10.1109/ICTACS56270.2022.9988683