Newborn face recognition using deep convolutional neural network

Newborn face recognition using deep convolutional neural network Development of expertise in Face Recognition has led researchers to apply its various techniques for newborn recognition as some of the problems such as swapping, kidnapping are still prevalent. The paper proposes to apply Deep Convolutional Neural Network(CNN) to IIT(BHU) newborn database. The database has its own advantages where the quality of images is high and segregation has been done for various expressions of newborn. The Deep CNN applied in this paper is more advantageous when compared to regular MLP. Along with this the results taken from application of proposed technique have been compared to state-of-the-art technique applied on the same database and it shows improved results. It has been found Deep CNN improves PCA by 22.09%, LDA by 12.98%, ICA by 11.35%, LBP by 17.08% and SURF by 10.8% for Neutral-Neutral faces. Along with this results have also been gathered to understand which Deep CNN architecture is most suitable for the database. The CNN architecture with 2 convolutional layers and 1 hidden layer is the best solution. The results have also been cross validated using 10-fold cross validation. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

Newborn face recognition using deep convolutional neural network

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Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-016-4342-x
Publisher site
See Article on Publisher Site

Abstract

Development of expertise in Face Recognition has led researchers to apply its various techniques for newborn recognition as some of the problems such as swapping, kidnapping are still prevalent. The paper proposes to apply Deep Convolutional Neural Network(CNN) to IIT(BHU) newborn database. The database has its own advantages where the quality of images is high and segregation has been done for various expressions of newborn. The Deep CNN applied in this paper is more advantageous when compared to regular MLP. Along with this the results taken from application of proposed technique have been compared to state-of-the-art technique applied on the same database and it shows improved results. It has been found Deep CNN improves PCA by 22.09%, LDA by 12.98%, ICA by 11.35%, LBP by 17.08% and SURF by 10.8% for Neutral-Neutral faces. Along with this results have also been gathered to understand which Deep CNN architecture is most suitable for the database. The CNN architecture with 2 convolutional layers and 1 hidden layer is the best solution. The results have also been cross validated using 10-fold cross validation.

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: Feb 27, 2017

References

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