Invariant moments based convolutional neural networks for image analysis

DOI

10.2991/ijcis.2017.10.1.62How to use a DOI?

Keywords

Zernike moments; convolution kernel; invariant moments; pattern recognition; hierarchical feature learning

Abstract

The paper proposes a method using convolutional neural network to effectively evaluate the discrimination between face and non face patterns, gender classification using facial images and facial expression recognition. The novelty of the method lies in the utilization of the initial trainable convolution kernels coefficients derived from the zernike moments by varying the moment order. The performance of the proposed method was compared with the convolutional neural network architecture that used random kernels as initial training parameters. The multilevel configuration of zernike moments was significant in extracting the shape information suitable for hierarchical feature learning to carry out image analysis and classification. Furthermore the results showed an outstanding performance of zernike moment based kernels in terms of the computation time and classification accuracy.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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TY  - JOUR
AU  - Vijayalakshmi G.V. Mahesh
AU  - Alex Noel Joseph Raj
AU  - Zhun Fan
PY  - 2017
DA  - 2017/01/01
TI  - Invariant moments based convolutional neural networks for image analysis
JO  - International Journal of Computational Intelligence Systems
SP  - 936
EP  - 950
VL  - 10
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2017.10.1.62
DO  - 10.2991/ijcis.2017.10.1.62
ID  - Mahesh2017
ER  -