  Gender classification (GC) has achieved high accuracy in different
experimental evaluations based mostly on inner facial details. However, these
results do not generalize well in unrestricted datasets and particularly in
cross-database experiments, where the performance drops drastically. In this
paper, 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 in
GROUPS. This dataset covers hard conditions such as low resolution imagery and
cluttered background. Firstly, we analyze in depth the performance of different
descriptors extracted from the face and its local context on this dataset.
Selecting the bests and studying their most suitable combination allows us to
design a solution that beats any previously published results for GROUPS with
the Dago's protocol, reaching an accuracy over 94.2%, reducing the gap with
other simpler datasets. The chosen solution based on local descriptors is later
evaluated in a cross-database scenario with the three mentioned datasets, and
full dataset 5-fold cross validation. The achieved results are compared with a
Convolutional Neural Network approach, achieving rather similar marks. Finally,
a solution is proposed combining both focuses, exhibiting great
complementarity, boosting GC performance to beat previously published results
in GC both cross-database, and full in-database evaluations.
