MassFace: an efficient implementation using triplet loss for face recognition
Yule Li
TL;DR
This paper introduces MassFace, an efficient face recognition method utilizing triplet loss, with experimental analysis on training factors, providing practical insights and open-source code for improved face recognition performance.
Contribution
It presents a practical implementation of triplet loss for face recognition, including analysis of training factors and insights to optimize its application.
Findings
Analyzed factors influencing triplet loss training
Provided practical guidelines for face recognition tasks
Released open-source code for MassFace implementation
Abstract
In this paper we present an efficient implementation using triplet loss for face recognition. We conduct the practical experiment to analyze the factors that influence the training of triplet loss. All models are trained on CASIA-Webface dataset and tested on LFW. We analyze the experiment results and give some insights to help others balance the factors when they apply triplet loss to their own problem especially for face recognition task. Code has been released in https://github.com/yule-li/MassFace.
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Taxonomy
TopicsFace recognition and analysis · Face and Expression Recognition · Biometric Identification and Security
MethodsTriplet Loss