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

2^B3^C: 2 Box 3 Crop of Facial Image for Gender Classification with Convolutional Networks