  Many real-time tasks, such as human-computer interaction, require fast and
efficient facial gender classification. Although deep CNN nets have been very
effective for a multitude of classification tasks, their high space and time
demands make them impractical for personal computers and mobile devices without
a powerful GPU. In this paper, we develop a 16-layer, yet lightweight, neural
network which boosts efficiency while maintaining high accuracy. Our net is
pruned from the VGG-16 model starting from the last convolutional (conv) layer
where we find neuron activations are highly uncorrelated given the gender.
Through Fisher's Linear Discriminant Analysis (LDA), we show that this high
decorrelation makes it safe to discard directly last conv layer neurons with
high within-class variance and low between-class variance. Combined with either
Support Vector Machines (SVM) or Bayesian classification, the reduced CNNs are
capable of achieving comparable (or even higher) accuracies on the LFW and
CelebA datasets than the original net with fully connected layers. On LFW, only
four Conv5_3 neurons are able to maintain a comparably high recognition
accuracy, which results in a reduction of total network size by a factor of 70X
with a 11 fold speedup. Comparisons with a state-of-the-art pruning method as
well as two smaller nets in terms of accuracy loss and convolutional layers
pruning rate are also provided.
