We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Gender classification based on isolated facial features and foggy faces using jointly trained deep convolutional neural network.
- Authors
Aslam, Aasma; Hussain, Babar; Cetin, Ahmet Enis; Umar, Arif Iqbal; Ansari, Rashid
- 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. © 2018 SPIE and IS&T [DOI: 10.1117/1.JEI.27.5.053023]
- Subjects
HUMAN facial recognition software; FACE perception; IMAGE recognition (Computer vision); IMAGE registration; DIGITAL image processing; IMAGE quality analysis
- Publication
Journal of Electronic Imaging, 2018, Vol 27, Issue 5, p1
- ISSN
1017-9909
- Publication type
Article
- DOI
10.1117/1.JEI.27.5.053023