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A Community Detection Approach to Cleaning Extremely Large Face Database.

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Researchers developed an automated method to clean noisy labels in large face datasets, creating the largest publicly available clean face dataset (C-MS-Celeb) for improved face recognition model training.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Building large face datasets is crucial for AI advancements.
  • Manual annotation is time-consuming and limits dataset size.
  • Automatic cleaning of noisy labels is highly desirable but challenging due to facial image diversity.

Purpose of the Study:

  • To propose an effective graph-based method for automatic cleaning of noisy labels in large-scale face datasets.
  • To create a large, clean face dataset to benefit face recognition research.
  • To demonstrate the positive impact of data cleaning on model performance.

Main Methods:

  • Utilized a graph-based approach incorporating community detection algorithms.
  • Employed deep Convolutional Neural Network (CNN) models for mislabeled image identification.
  • Applied the method to the MS-Celeb-1M dataset, resulting in the C-MS-Celeb dataset.

Main Results:

  • Successfully cleaned the MS-Celeb-1M dataset, creating C-MS-Celeb with 6,464,018 images of 94,682 celebrities.
  • The cleaning process preserved data diversity while ensuring high cleanness.
  • Training a model on C-MS-Celeb achieved a 99.67% Equal Error Rate on the LFW benchmark without fine-tuning.

Conclusions:

  • The proposed graph-based cleaning method effectively addresses noisy labels in large face datasets.
  • The C-MS-Celeb dataset is the largest publicly available clean face dataset, significantly advancing face recognition research.
  • Data cleaning has a demonstrably positive effect on the performance of face recognition models.