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Gender classification (GC) has achieved high accuracy in differentexperimental evaluations based mostly on inner facial details. However, theseresults do not generalize well in unrestricted datasets and particularly incross-database experiments, where the performance drops drastically. In thispaper, we analyze the state-of-the-art GC accuracy on three large datasets:MORPH, LFW and GROUPS. We discuss their respective difficulties and bias,concluding that the most challenging and wildest complexity is present inGROUPS. This dataset covers hard conditions such as low resolution imagery andcluttered background. Firstly, we analyze in depth the performance of differentdescriptors extracted from the face and its local context on this dataset.Selecting the bests and studying their most suitable combination allows us todesign a solution that beats any previously published results for GROUPS withthe Dago's protocol, reaching an accuracy over 94.2%, reducing the gap withother simpler datasets. The chosen solution based on local descriptors is laterevaluated in a cross-database scenario with the three mentioned datasets, andfull dataset 5-fold cross validation. The achieved results are compared with aConvolutional Neural Network approach, achieving rather similar marks. Finally,a solution is proposed combining both focuses, exhibiting greatcomplementarity, boosting GC performance to beat previously published resultsin GC both cross-database, and full in-database evaluations.

Descriptors and regions of interest fusion for gender classification in the wild. Comparison and combination with Convolutional Neural Networks