# Small-Scale Face Detection Based on Improved R-FCN
## 1 Introduction  
Many achievements have been made in small-scale face detection, which can be divided into two aspects. One is to preprocess the input image. For example, in [1], a scale proposal network was designed to estimate the scale of a face; then, the input image was resized and sent to the network. In [2], a generative adversarial network was used to reconstruct small-scale faces. Both methods mentioned above increase the computational cost. The second idea is to improve the ability to detect small-scale faces from the network itself, such as enhancing the expression ability of features. For example, [PERSON] j et al. [3] adopted a dual shot face detector to merge a feature pyramid network (FPN) [4] and receptive field block (RFB) [5] to enhance the characteristics of the shared feature map. Although the detection precision is improved through the dual-channel detection method, the detection speed needs to be improved. [PERSON] et al. [6] proposed receptive field enhancement module (RFEM) to generate receptive fields with various shapes to capture faces with extreme poses, but the effect of RFEM is insignificant. Considering that image preprocessing is time consuming, we improve the precision of face detection from the network itself.  
In recent years, the face-detection algorithm based on faster region-based convolutional neural network (faster R-CNN) [7] has made great progress. However, faster R-CNN destroys the spatial structure of features and makes the network insensitive to the position of the faces because it uses thefully connected layer for classification and detection. As a result, [PERSON] et al. [2018] proposed region-based fully convolutional networks (R-FCN) and adopted a fully convolutional network (FCN) to encode the position information of the target in the region of interest (RoI) to solve the problems of classification invariance and position sensitivity of faster R-CNN. R-FCN has good detection performance on pattern analysis, statistical modelling and computational learning visual object classes (PASCAL VOC), and its detection speed is faster than that of faster R-CNN. Therefore, we adopt R-FCN to detect small-scale faces. The framework of R-FCN is shown in Figure 1. We propose a novel R-FCN framework based on R-FCN, which is shown in Figure 1. We redesign the feature extraction network, which includes the original feature extraction network branch of R-FCN and a new feature fusion branch. The new feature fusion branch is used to fully fuse the local information of the features extracted from the low and middle layers of the original feature extraction network branch and the semantic information of the features extracted from the top layer of the original feature extraction network branch. The improved R-FCN is called R-FCN with a feature fusion branch (f-R-FCN). In addition, we add a receptive field adaptation block (RFAB) based on f-R-FCN to enhance the discrimination of the features of small-scale faces. The improved f-R-FCN is called R-FCN with a feature fusion branch and RFAB (RFAB-f-R-FCN). In Figure 1, a region proposal network (RPN) is a fully convolutional neural network (CNN), which can generate \(k\) boxes with different scale and aspect ratio with each pixel as the center in the feature map, called anchor. After the anchors are classified and regressed, the corresponding region in the original image is called the proposal.  
The main contributions of this study are as follows:  
1. We propose a novel R-FCN framework, that is, we add a feature fusion branch and RFAB in original R-FCN.
2. A new feature fusion method is proposed to alleviate the problem of low detection rate caused by the original R-FCN.
3. RFAB is proposed to enhance the ability of feature expression of small-scale faces.  
Figure 1: The framework of region-based fully convolutional networks (R-FCN) and small-scale face detection based on R-FCN.  
We improve the anchor setting method and adopt soft non-maximum suppression (SoftNMS) as the selection method of candidate boxes.