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.
Minimalistic CNN-based ensemble model for gender prediction from face images
Pattern Recognition Letters, 15 January 2016, Vol.70
Type:
Journal
Date:
2016-01-15
Department:
Sécurité numérique
Eurecom Ref:
4768
Copyright:
© Elsevier. Personal use of this material is permitted. The definitive version of this paper was published in Pattern Recognition Letters, 15 January 2016, Vol.70 and is available at : http://dx.doi.org/10.1016/j.patrec.2015.11.011
See also:
PERMALINK : https://www.eurecom.fr/publication/4768