Face-MagNet: Magnifying Feature Maps to Detect Small Faces
This addresses the challenge of small face detection in computer vision, with incremental improvements over existing methods.
The paper tackles the problem of detecting small faces by introducing Face-MagNet, a face detector based on Faster-RCNN that uses deconvolution layers to magnify feature maps, achieving better results than a ResNet101-based method on the WIDER dataset.
In this paper, we introduce the Face Magnifier Network (Face-MageNet), a face detector based on the Faster-RCNN framework which enables the flow of discriminative information of small scale faces to the classifier without any skip or residual connections. To achieve this, Face-MagNet deploys a set of ConvTranspose, also known as deconvolution, layers in the Region Proposal Network (RPN) and another set before the Region of Interest (RoI) pooling layer to facilitate detection of finer faces. In addition, we also design, train, and evaluate three other well-tuned architectures that represent the conventional solutions to the scale problem: context pooling, skip connections, and scale partitioning. Each of these three networks achieves comparable results to the state-of-the-art face detectors. With extensive experiments, we show that Face-MagNet based on a VGG16 architecture achieves better results than the recently proposed ResNet101-based HR method on the task of face detection on WIDER dataset and also achieves similar results on the hard set as our other method SSH.