Automatically Building Face Datasets of New Domains from Weakly Labeled Data with Pretrained Models

TL;DR

This paper introduces an automated method to construct high-quality face datasets from large-scale weakly labeled internet data using pretrained models, improving face recognition accuracy.

Contribution

The paper presents a novel incremental dataset cleaning approach leveraging graph-based verification and model updates to enhance face recognition datasets.

Findings

Achieved 99.7% purity in cleaned dataset

Recognition rate of 93.1% on Asian faces

Outperformed public dataset CASIA in recognition accuracy

Abstract

Training data are critical in face recognition systems. However, labeling a large scale face data for a particular domain is very tedious. In this paper, we propose a method to automatically and incrementally construct datasets from massive weakly labeled data of the target domain which are readily available on the Internet under the help of a pretrained face model. More specifically, given a large scale weakly labeled dataset in which each face image is associated with a label, i.e. the name of an identity, we create a graph for each identity with edges linking matched faces verified by the existing model under a tight threshold. Then we use the maximal subgraph as the cleaned data for that identity. With the cleaned dataset, we update the existing face model and use the new model to filter the original dataset to get a larger cleaned dataset. We collect a large weakly labeled dataset containing 530,560 Asian face images of 7,962 identities from the Internet, which will be published for the study of face recognition. By running the filtering process, we obtain a cleaned datasets (99.7+% purity) of size 223,767 (recall 70.9%). On our testing dataset of Asian faces, the model trained by the cleaned dataset achieves recognition rate 93.1%, which obviously outperforms the model trained by the public dataset CASIA whose recognition rate is 85.9%.