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, 243–249, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W4-243-2017
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W4, 243–249, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W4-243-2017

  10 May 2017

10 May 2017

MULTI-PATCHES IRIS BASED PERSON AUTHENTICATION SYSTEM USING PARTICLE SWARM OPTIMIZATION AND FUZZY C-MEANS CLUSTERING

B. H. Shekar1 and S. S. Bhat2 B. H. Shekar and S. S. Bhat
  • 1Mangalore University, Mangalore, Karnataka, India
  • 2Government Arts and Science College, Karwar, Karnataka, India

Keywords: Particle swarm optimization, Fuzzy c-means, Taylor’s series expansion, weighted mean Hamming distance, Iris recognition system

Abstract. Locating the boundary parameters of pupil and iris and segmenting the noise free iris portion are the most challenging phases of an automated iris recognition system. In this paper, we have presented person authentication frame work which uses particle swarm optimization (PSO) to locate iris region and circular hough transform (CHT) to device the boundary parameters. To undermine the effect of the noise presented in the segmented iris region we have divided the candidate region into N patches and used Fuzzy c-means clustering (FCM) to classify the patches into best iris region and not so best iris region (noisy region) based on the probability density function of each patch. Weighted mean Hammimng distance is adopted to find the dissimilarity score between the two candidate irises. We have used Log-Gabor, Riesz and Taylor’s series expansion (TSE) filters and combinations of these three for iris feature extraction. To justify the feasibility of the proposed method, we experimented on the three publicly available data sets IITD, MMU v-2 and CASIA v-4 distance.