2^B3^C: 2 Box 3 Crop of Facial Image for Gender Classification with Convolutional Networks
This work addresses gender classification for facial recognition applications, but it is incremental as it builds on existing CNN architectures with minor modifications.
The paper tackled gender classification in facial images by proposing a method that uses face detection, margin expansion, and multiple cropping schemes with a pretrained VGG-16 CNN, achieving 90.8% accuracy on Adience and 95.3% on LFW datasets.
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.