  In this paper, we tackle the classification of gender in facial images with
deep learning. Our convolutional neural networks (CNN) use the VGG-16
architecture [1] and are pretrained on ImageNet for image classification. Our
proposed method (2^B3^C) first detects the face in the facial image, increases
the margin of a detected face by 50%, cropping the face with two boxes three
crop schemes (Left, Middle, and Right crop) and extracts the CNN predictions on
the cropped schemes. The CNNs of our method is fine-tuned on the Adience and
LFW with gender annotations. We show the effectiveness of our method by
achieving 90.8% classification on Adience and achieving competitive 95.3%
classification accuracy on LFW dataset. In addition, to check the true ability
of our method, our gender classification system has a frame rate of 7-10 fps
(frames per seconds) on a GPU considering real-time scenarios.
