  Local deep neural networks have been recently introduced for gender
recognition. Although, they achieve very good performance they are very
computationally expensive to train. In this work, we introduce a simplified
version of local deep neural networks which significantly reduces the training
time. Instead of using hundreds of patches per image, as suggested by the
original method, we propose to use 9 overlapping patches per image which cover
the entire face region. This results in a much reduced training time, since
just 9 patches are extracted per image instead of hundreds, at the expense of a
slightly reduced performance. We tested the proposed modified local deep neural
networks approach on the LFW and Adience databases for the task of gender and
age classification. For both tasks and both databases the performance is up to
1% lower compared to the original version of the algorithm. We have also
investigated which patches are more discriminative for age and gender
classification. It turns out that the mouth and eyes regions are useful for age
classification, whereas just the eye region is useful for gender
classification.
