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Gender classification from face images using central difference convolutional networks

Gender classification from face images using central difference convolutional networks Nowadays gender classification which plays a vital role in face recognition systems is one of the main matters in computer vision. It is difficult to classify the gender from facial images when dealing with unconstrained images in a cross-dataset protocol. In this work, we propose two convolutional neural networks where one of the networks used the central difference convolution layer and another network used the vanilla convolution layer. The system was trained with the Casia WebFace dataset and tested on two cross-datasets, labeled faces in the wild (LFW) and FEI dataset. It is worth mentioning that the experimental results show the power and effectiveness of the proposed method. This method obtains a classification rate of 97.79% for the LFW dataset and 99.10% for the FEI dataset. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Multimedia Information Retrieval Springer Journals

Gender classification from face images using central difference convolutional networks

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Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
2192-6611
eISSN
2192-662X
DOI
10.1007/s13735-022-00259-0
Publisher site
See Article on Publisher Site

Abstract

Nowadays gender classification which plays a vital role in face recognition systems is one of the main matters in computer vision. It is difficult to classify the gender from facial images when dealing with unconstrained images in a cross-dataset protocol. In this work, we propose two convolutional neural networks where one of the networks used the central difference convolution layer and another network used the vanilla convolution layer. The system was trained with the Casia WebFace dataset and tested on two cross-datasets, labeled faces in the wild (LFW) and FEI dataset. It is worth mentioning that the experimental results show the power and effectiveness of the proposed method. This method obtains a classification rate of 97.79% for the LFW dataset and 99.10% for the FEI dataset.

Journal

International Journal of Multimedia Information RetrievalSpringer Journals

Published: Sep 22, 2022

Keywords: Gender classification; Central difference convolution; Face recognition; Convolutional neural networks

References