TY  - JOUR
AU  - Pau Rodriguez
AU  - Guillem Cucurull
AU  - Josep M. Gonfaus
AU  - Xavier Roca
AU  - Jordi Gonzalez
PY  - 2017//
TI  - Age and gender recognition in the wild with deep attention
T2  - PR
JO  - Pattern Recognition
SP  - 563
EP  - 571
VL  - 72
KW  - Age recognition
KW  - Gender recognition
KW  - Deep neural networks
KW  - Attention mechanisms
N2  - 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.
UR  - https://doi.org/10.1016/j.patcog.2017.06.028
L1  - http://refbase.cvc.uab.es/files/RCG2017b.pdf
N1  - ISE; 600.098; 602.133; 600.119
ID  - Pau Rodriguez2017
ER  -