MATEC Web Conf.
Volume 232, 20182018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
|Number of page(s)||4|
|Section||Network Security System, Neural Network and Data Information|
|Published online||19 November 2018|
Dual Minkowski Loss for Face Verification of Convolutional Network
School of Maritime Economics and Management of Dalian Maritime University, Dalian Maritime University, Dalian 116026, China
2 City Institute, Dalian University of Technology, Dalian 116600, China
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%.
© The Authors, published by EDP Sciences, 2018
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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