Multi-view face detection in videos with online adaptation

Yao Chuan Chang, Yen-Yu Lin, Hong Yuan Mark Liao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Most learning-based approaches to face detection suffer from the problem of performance degradation on faces that are not covered by training data. However, including all variations of faces in training is practically infeasible due to the scalability restriction of machine learning algorithms and expensive manual labeling. In this work, we focus on face detection in videos, and alleviate this problem by exploiting strong correlation among video frames. We augment a pre-trained multiview face detection with an incrementally derived Gaussian process regressor. The regressor can extract and propagate visual knowledge across frames, and adapts the detector to handle unseen faces. Testing on two datasets, the promising results manifest the effectiveness of the proposed approach.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
Pages3949-3953
Number of pages5
DOIs
StatePublished - 1 Dec 2013
Event2013 20th IEEE International Conference on Image Processing, ICIP 2013 - Melbourne, VIC, Australia
Duration: 15 Sep 201318 Sep 2013

Publication series

Name2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings

Conference

Conference2013 20th IEEE International Conference on Image Processing, ICIP 2013
CountryAustralia
CityMelbourne, VIC
Period15/09/1318/09/13

Keywords

  • boosting
  • Face detection
  • Gaussian process regression
  • transfer learning
  • video analysis

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  • Cite this

    Chang, Y. C., Lin, Y-Y., & Liao, H. Y. M. (2013). Multi-view face detection in videos with online adaptation. In 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings (pp. 3949-3953). [6738813] (2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings). https://doi.org/10.1109/ICIP.2013.6738813