Dual Minkowski Loss for Face Verification of Convolutional Network

• Published in: MATEC Web of Conferences
• Main Authors: Wang Dandan, Chen Yan
• Format: Article in Journal/Newspaper
• Language: English; French
• Published: EDP Sciences 2018
• Subjects: Engineering (General). Civil engineering (General); TA1-2040; DML
• Online Access: https://doi.org/10.1051/matecconf/201823201007; https://doaj.org/article/a959fb0834054b89aa9d7ab8a5803f63

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%.