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PRL
2016

Minimalistic CNN-based ensemble model for gender prediction from face images

3 years 11 days ago
Minimalistic CNN-based ensemble model for gender prediction from face images
Despite being extensively studied in the literature, the problem of gender recognition from face images remains difficult when dealing with unconstrained images in a cross-dataset protocol. In this work, we propose a convolutional neural network ensemble model to improve the state-of-the-art accuracy of gender recognition from face images on one of the most challenging face image datasets today, LFW (Labeled Faces in the Wild). We find that convolutional neural networks need significantly less training data to obtain the state-of-the-art performance than previously proposed methods. Furthermore, our ensemble model is deliberately designed in a way that both its memory requirements and running time are minimized. This allows us to envision a potential usage of the constructed model in embedded devices or in a cloud platform for an intensive use on massive image databases. c 2015 Elsevier Ltd. All rights reserved.
Grigory Antipov, Sid-Ahmed Berrani, Jean-Luc Dugel
Added 09 Apr 2016
Updated 09 Apr 2016
Type Journal
Year 2016
Where PRL
Authors Grigory Antipov, Sid-Ahmed Berrani, Jean-Luc Dugelay
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