Local Deep Neural Networks for Age and Gender Classification
This work addresses computational efficiency for researchers and practitioners in facial analysis, but it is incremental as it modifies an existing method with minor performance trade-offs.
The authors tackled the high computational cost of training local deep neural networks for gender and age classification by proposing a simplified version using only 9 overlapping patches per image instead of hundreds, which reduced training time but resulted in up to a 1% performance drop on LFW and Adience databases.
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.