High-order information for robust iris recognition under less controlled conditions

Guanglei Yang, Hui Zeng, Peihua Li, Lei Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

7 Citations (Scopus)


Iris recognition has achieved great progress in cooperative environments in the past decades. However, in less controlled conditions it is still an open and challenging problem because of severe noisy factors induced by non-cooperative subjects. For handling this challenging problem, we propose a method called ordinal measure of outer product tensor (O2PT) which leverages the high-order information of image features. O2PT consists of two components. First we compute outer product tensors of raw features (e.g. SIFT) which are vectorized and locally aggregated, characterizing the second-order statistics of raw features. And then we compute the ordinal measure of the aggregated outer product tensors to model the order relation of iris texture, which makes the representation more compact and robust to noise and illumination changes. Furthermore, we combine two modalities to improve the matching performance, namely, O2PT for iris image matching and Fisher Vector (FV), which also exploits the high-order information, for eye image matching. We have achieved competitive matching performance on the challenging UBIRIS.v2 and CASIA-Iris-Thousand databases.
Original languageEnglish
Title of host publication2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781479983391
Publication statusPublished - 9 Dec 2015
EventIEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada
Duration: 27 Sep 201530 Sep 2015


ConferenceIEEE International Conference on Image Processing, ICIP 2015
CityQuebec City


  • Fisher vector (FV)
  • Iris recognition
  • ordinal measure of outer product tensor (O PT) 2

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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