Dual Minkowski Loss for Face Verification of Convolutional Network

摘要：

　　Despite face recognition and verification have achieved great success in recent years,these achievements are experimental results on fixed data sets.Implementing these outstanding technologies in the field of undeveloped data sets presents serious challenges.We adopt three state-of-the-art pre-trained models on an entire new dataset University Test System Database(UTSD),however the results are far from satisfactory.Therefore,two methods are adopted to solve this problem.The first way is data augmentation including horizontal flipping,cropping and RGB channels transform,which can solve the imbalance of label pairs.The second way is the combination of Manhattan Distance and Euclidean Distance,we call it Dual Minkowski Loss(DML).Through the implementation of photo augmentation and innovative method on UTSD,the accuracy of face verification has been significantly improved,achieving the best 99.3%.

作者: Wang Dandan Chen Yan

作者单位: School of Maritime Economics and Management of Dalian Maritime University,Dalian Maritime University School of Maritime Economics and Management of Dalian Maritime University,Dalian Maritime University

会议类型: 国际会议

会议名称: 2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)(2018第二届电子信息技术与计算机工程国际会议)(EITCE2018)

会议地点: 上海

会议语种:英文

页码: 1-4

在线出版日期: 2018-10-12（万方平台首次上网日期，不代表论文的发表时间）