Optimizing the selection of a biometric template from a sequence

Gneushev, A. N.; Kovkov, D. V.; Матвеев, И. А.; Novik, Vladimir P.

doi:10.1134/s1064230715030077

Cited by 4 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thorough investigation of feature vectors obtained from iris showed that not all the vector elements are equally relevant (Hollingsworth et al, 2009, Gneushev et al, 2015. There are two characteristics which determine the element utility: fragility and discriminability (Proença, 2015).…”

Section: Introductionmentioning

confidence: 99%

Feature Vector Construction Method for Iris Recognition

Odinokikh

Fartukov

Korobkin³

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%