PT Journal
AU Pau Rodriguez
   Guillem Cucurull
   Josep M. Gonfaus
   Xavier Roca
   Jordi Gonzalez
TI Age and gender recognition in the wild with deep attention
SO Pattern Recognition
JI PR
PY 2017
BP 563
EP 571
VL 72
DE Age recognition; Gender recognition; Deep neural networks; Attention mechanisms
AB Face analysis in images in the wild still pose a challenge for automatic age and gender recognition tasks, mainly due to their high variability in resolution, deformation, and occlusion. Although the performance has highly increased thanks to Convolutional Neural Networks (CNNs), it is still far from optimal when compared to other image recognition tasks, mainly because of the high sensitiveness of CNNs to facial variations. In this paper, inspired by biology and the recent success of attention mechanisms on visual question answering and fine-grained recognition, we propose a novel feedforward attention mechanism that is able to discover the most informative and reliable parts of a given face for improving age and gender classification. In particular, given a downsampled facial image, the proposed model is trained based on a novel end-to-end learning framework to extract the most discriminative patches from the original high-resolution image. Experimental validation on the standard Adience, Images of Groups, and MORPH II benchmarks show that including attention mechanisms enhances the performance of CNNs in terms of robustness and accuracy.
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