Small-Scale Face Detection Based on Improved R-FCN

Chaowei Tang,Haotian Wen,Shiyu Chen,Xu Zhou,Shuai Ruan

doi:10.3390/app10124177

Abstract

Face detection is an important basic technique for face-related applications, such as face analysis, recognition, and reconstruction. Images in unconstrained scenes may contain many small-scale faces. The features that the detector can extract from small-scale faces are limited, which will cause missed detection and greatly reduce the precision of face detection. Therefore, this study proposes a novel method to detect small-scale faces based on region-based fully convolutional network (R-FCN). First, we propose a novel R-FCN framework with the ability of feature fusion and receptive field adaptation. Second, a bottom-up feature fusion branch is established to enrich the local information of high-layer features. Third, a receptive field adaptation block (RFAB) is proposed to ensure that the receptive field can be adaptively selected to strengthen the expression ability of features. Finally, we improve the anchor setting method and adopt soft non-maximum suppression (SoftNMS) as the selection method of candidate boxes. Experimental results show that average precision for small-scale face detection of R-FCN with feature fusion branch and RFAB (RFAB-f-R-FCN) is improved by 0.8%, 2.9%, and 11% on three subsets of Wider Face compared with that of R-FCN.

Highlights

Many achievements have been made in small-scale face detection, which can be divided into two aspects
The 3, reason is that and we find that average precision of detection on than that is on . we Creason thethat feature fusion branch (AP)
After changing non-maximum suppression (NMS) to soft non-maximum suppression (SoftNMS), AP increases by 1%, 3.3%, and 4.1% on three subsets, respectively, which verifies the effectiveness of SoftNMS

Summary

Introduction

Many achievements have been made in small-scale face detection, which can be divided into two aspects. In [1], a scale proposal network was designed to estimate the scale of a face; 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. Considering that image preprocessing is time consuming, we improve the precision of face detection from the network itself

Methods

Results

Conclusion