A Multi-scale Face Detection Algorithm Based on Improved SSD model

摘要：

　　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.

关键词： Multi-scale face detection Feature-dense connection strategy Contextual information Feature map fusion SSD model

作者: Di Fan Shuai Fang Xiaoxin Liu Yongyi Li Shang Gao

作者单位: Shandong University of Science and Technology Qingdao Shandong Province,China Shandong University of Science and Technology Qingdao,Shandong Province,China

会议类型: 国际会议

会议名称: 2019国图灵大会(ACM Turing Celebration conference-China 2019 )

会议地点: 成都

会议语种:英文

页码: 307-315

在线出版日期: 2019-05-17（万方平台首次上网日期，不代表论文的发表时间）