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Incremental Training for Face Recognition

  • Martin Winter
  • Werner BailerEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11295)

Abstract

Many applications require the identification of persons in video. However, the set of persons of interest is not always known in advance, e.g., in applications for media production and archiving. Additional training samples may be added during the analysis, or groups of faces of one person may need to be identified retrospectively. In order to avoid re-running the face recognition, we propose an approach that supports fast incremental training based on a state of the art face detection and recognition pipeline using CNNs and an online random forest as a classifier. We also describe an algorithm to use the incremental training approach to automatically train classifiers for unknown persons, including safeguards to avoid noise in the training data. We show that the approach reaches state of the art performance on two datasets when using all training samples, but performs better with few or even only one training sample.

Notes

Acknowledgments

The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements no 732461, ReCAP (“Real-time Content Analysis and Processing”, http://recap-project.com), and 761802, MARCONI (“Multimedia and Augmented Radio Creation: Online, iNteractive, Individual”, https://www.projectmarconi.eu).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.JOANNEUM RESEARCH Forschungsgesellschaft mbH, DIGITAL – Institute for Information and Communication TechnologiesGrazAustria

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