Abstract

Detecting eyes in images is fundamental for many computer vision applications including face detection, face recognition, and human-computer interaction. Most existing methods are designed and tested on datasets acquired under controlled lab settings (e.g., fixed scale, known poses, clean background, etc.), leaving their performance to be further examined on real-world, uncontrolled images, such as on-line images. This paper presents an effort on developing a fast and accurate eye detector for on-line images for which the acquisition condition is unknown and varies from one image to another, resulting in unpredictable background and variable scales for the eyes/faces. The key idea is to develop a scale-adaptive EigenEye approach, which employs an approximate scale estimated from face detection to modulate the pre-trained EigenEye basis in searching for the best match in a test image. The effort also includes building a 2845-image dataset with accurately-annotated eye locations and size, which will be made public to the community for future comparative study. Evaluation using this dataset, with comparison with a few leading state-of-the-art approaches, demonstrates the advantages of the proposed method.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
PublisherIEEE Computer Society
Pages2911-2915
Number of pages5
Volume2016-August
ISBN (Electronic)9781467399616
DOIs
StatePublished - Aug 3 2016
Event23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States
Duration: Sep 25 2016Sep 28 2016

Other

Other23rd IEEE International Conference on Image Processing, ICIP 2016
CountryUnited States
CityPhoenix
Period9/25/169/28/16

Fingerprint

Face recognition
Human computer interaction
Computer vision
Detectors

Keywords

  • Eye detection
  • Feature extraction
  • Object detection
  • Scale adaptive

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

Cite this

Zhou, X., Wang, Y., Zhang, P., & Li, B. (2016). Scale-adaptive EigenEye for fast eye detection in wild web images. In 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings (Vol. 2016-August, pp. 2911-2915). [7532892] IEEE Computer Society. https://doi.org/10.1109/ICIP.2016.7532892

Scale-adaptive EigenEye for fast eye detection in wild web images. / Zhou, Xu; Wang, Yilin; Zhang, Peng; Li, Baoxin.

2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings. Vol. 2016-August IEEE Computer Society, 2016. p. 2911-2915 7532892.

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

Zhou, X, Wang, Y, Zhang, P & Li, B 2016, Scale-adaptive EigenEye for fast eye detection in wild web images. in 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings. vol. 2016-August, 7532892, IEEE Computer Society, pp. 2911-2915, 23rd IEEE International Conference on Image Processing, ICIP 2016, Phoenix, United States, 9/25/16. https://doi.org/10.1109/ICIP.2016.7532892
Zhou X, Wang Y, Zhang P, Li B. Scale-adaptive EigenEye for fast eye detection in wild web images. In 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings. Vol. 2016-August. IEEE Computer Society. 2016. p. 2911-2915. 7532892 https://doi.org/10.1109/ICIP.2016.7532892
Zhou, Xu ; Wang, Yilin ; Zhang, Peng ; Li, Baoxin. / Scale-adaptive EigenEye for fast eye detection in wild web images. 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings. Vol. 2016-August IEEE Computer Society, 2016. pp. 2911-2915
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