Gender classification based on isolated facial features and foggy faces using jointly trained deep convolutional neural network

Aasma Aslam; Babar Hussain; Ahmet Enis Cetin; Arif Iqbal Umar; Rashid Ansari

doi:10.1117/1.JEI.27.5.053023

27 September 2018 Gender classification based on isolated facial features and foggy faces using jointly trained deep convolutional neural network

Aasma Aslam, Babar Hussain, Ahmet Enis Cetin, Arif Iqbal Umar, Rashid Ansari

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 5, 053023 (September 2018). https://doi.org/10.1117/1.JEI.27.5.053023

Abstract

Gender classification, a two-class problem (male or female), has been the subject of extensive research recently and gained a lot of attention due to its varied set of applications. The proposed work relies on individual facial features to train a convolutional neural network (CNN) for gender classification. In contrast with previously reported results that assume the facial features are independent, we consider the facial features as correlated features by training a single CNN that jointly learns from all facial features. In terms of accuracy, our results either outperform, or are on par with, other gender classification techniques applied to three different datasets namely specs on faces, groups, and face recognition technology. In terms of performance, the proposed CNN has significantly fewer parameters as compared with other techniques reported in the literature. Our learnable parameters are fewer than those required in techniques reported in recent work, which enables them to make the network less sensitive to over-fitting and easier to train than techniques that use different CNNs for each facial feature as reported in the literature.

Citation Download Citation

Aasma Aslam, Babar Hussain, Ahmet Enis Cetin, Arif Iqbal Umar, and Rashid Ansari "Gender classification based on isolated facial features and foggy faces using jointly trained deep convolutional neural network," Journal of Electronic Imaging 27(5), 053023 (27 September 2018). https://doi.org/10.1117/1.JEI.27.5.053023

Received: 17 April 2018; Accepted: 4 September 2018; Published: 27 September 2018

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