The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-2/W4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W4, 233–236, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W4-233-2017
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W4, 233–236, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W4-233-2017

  10 May 2017

10 May 2017

FEATURE VECTOR CONSTRUCTION METHOD FOR IRIS RECOGNITION

G. Odinokikh1,4, A. Fartukov1, M. Korobkin3, and J. Yoo2 G. Odinokikh et al.
  • 1Samsung R&D Institute Russia, 127018, Dvintsev st. 12, Moscow, Russian Federation
  • 2Samsung Electronics Co. Ltd., 457-4 Maetan 3-dong, Suwon, South Korea
  • 3MIEE, 124498, Shokin sq., Zelenograd, Russia
  • 4Dorodnicyn Computing Centre of RAS, 119333, Vavilov st. 40, Moscow, Russia

Keywords: Iris recognition, Iris feature extraction, Biometrics, Mobile biometrics, Quantization, Encoding

Abstract. One of the basic stages of iris recognition pipeline is iris feature vector construction procedure. The procedure represents the extraction of iris texture information relevant to its subsequent comparison. Thorough investigation of feature vectors obtained from iris showed that not all the vector elements are equally relevant. There are two characteristics which determine the vector element utility: fragility and discriminability. Conventional iris feature extraction methods consider the concept of fragility as the feature vector instability without respect to the nature of such instability appearance. This work separates sources of the instability into natural and encodinginduced which helps deeply investigate each source of instability independently. According to the separation concept, a novel approach of iris feature vector construction is proposed. The approach consists of two steps: iris feature extraction using Gabor filtering with optimal parameters and quantization with separated preliminary optimized fragility thresholds. The proposed method has been tested on two different datasets of iris images captured under changing environmental conditions. The testing results show that the proposed method surpasses all the methods considered as a prior art by recognition accuracy on both datasets.