A multi-scale face detection algorithm based on improved SSD model
Di Fan, Shuai Fang, Xiaoxin Liu, Yongyi Li, Shang Gao · Proceedings of the ACM Turing Celebration Conference - China · 2019
At present, the face detection model based on single convolutional neural network has the problem of the low accuracy of small-scale face detection when solving the problem of face detection at different scales. So, we propose an improved multi-scale face detection method based on SSD. The method adopts the feature-dense connection strategy to improve the network structure of the basic network in the SSD model, strengthening the information mobility between different convolutional layers and improving the feature description ability of the basic network. Then, the detection accuracy of small-scale faces is improved by introducing context information into shallow features. We evaluate our proposed architecture on WIDER FACE dataset, and it achieves a high average precision (AP) of 73.1%, 90% and 92% for different data sets ("difficult", "medium" and "simple") respectively, which is higher than several other methods.