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.
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Sheikh Fathollahi, M., Heidari, R. Gender classification from face images using central difference convolutional networks. Int J Multimed Info Retr 11, 695–703 (2022). https://doi.org/10.1007/s13735-022-00259-0
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DOI: https://doi.org/10.1007/s13735-022-00259-0