AI Chat AI Image Generator AI Video Text to Speech

MassFace: an efficient implementation using triplet loss for face recognition

02/28/2019

∙

by Yule Li, et al.

∙

∙

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.

page 1

page 2

page 3

page 4

research

∙ 05/11/2019

Triplet Distillation for Deep Face Recognition

Convolutional neural networks (CNNs) have achieved a great success in fa...

0 Yushu Feng, et al. ∙

research

∙ 11/14/2017

TripletGAN: Training Generative Model with Triplet Loss

As an effective way of metric learning, triplet loss has been widely use...

0 Gongze Cao, et al. ∙

research

∙ 07/19/2017

Learning Unified Embedding for Apparel Recognition

In apparel recognition, specialized models (e.g. models trained for a pa...

0 Yang Song, et al. ∙

research

∙ 03/12/2021

Cross-Domain Similarity Learning for Face Recognition in Unseen Domains

Face recognition models trained under the assumption of identical traini...

0 Masoud Faraki, et al. ∙

research

∙ 06/10/2020

Unique Faces Recognition in Videos

This paper tackles face recognition in videos employing metric learning ...

0 Jiahao Huo, et al. ∙

research

∙ 07/27/2021

MixFaceNets: Extremely Efficient Face Recognition Networks

In this paper, we present a set of extremely efficient and high throughp...

0 Fadi Boutros, et al. ∙

research

∙ 07/31/2018

The Devil of Face Recognition is in the Noise

The growing scale of face recognition datasets empowers us to train stro...

2 Fei Wang, et al. ∙