Iris-Based Personal Authentication Using a Normalized Directional Energy Feature

Park, Chul-Hyun; Lee, Joon-Jae; Smith, M.J.T.; Park, Kil−Houm

doi:10.1007/3-540-44887-x_27

Cited by 61 publications

(30 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar to the matching scheme of Daugman, they sampled binary emergent frequency functions to form a feature vector and used Hamming distance for matching. Park et al [24] used a directional filter bank to decompose an iris image into eight directional subband outputs and extracted the normalized directional energy as features. Iris matching was performed by computing Euclidean distance between the input and the template feature vectors.…”

Section: Related Workmentioning

confidence: 99%

Iris Compression and Recognition using Spherical Geometry Image

Ramadan¹

2015

ijarai

View full text Add to dashboard Cite

Abstract-this research is considered to be a research to attract attention to the 3D iris compression to store the database of the iris. Actually, the 3D iris database cannot be found and in trying to solve this problem 2D iris database images are converted to 3D images just to implement the compression techniques used in 3D domain to test it and give an approximation results or to focus on this new direction in research. In this research a fully automated 3D iris compression and recognition system is presented. We use spherical based wavelet coefficients for efficient representation of the 3D iris. The spherical wavelet transformation is used to decompose the iris image into multi-resolution sub images. The representation of features based on spherical wavelet parameterization of the iris image was proposed for the 3D iris compression system. To evaluate the performance of the proposed approach, experiments were performed on the CASIA Iris database. Experimental results show that the spherical wavelet coefficients yield excellent compression capabilities with minimal set of features. Haar wavelet coefficients extracted from the iris image was found to generate good recognition results.

show abstract

Section: Related Workmentioning

confidence: 99%

Iris Compression and Recognition using Spherical Geometry Image

Ramadan¹

2015

ijarai

View full text Add to dashboard Cite

show abstract

“…These methods use local and global features of the iris. Using phase based approach [3][4][5][6]14,17,18], wavelet transform zero crossing approach [9,[19][20][21], texture analysis based methods [7,8,12,22,23] the solving of the iris recognition problem is considered. In [24][25][26][27] independent component analysis is proposed for iris recognition.…”

Section: Introductionmentioning

confidence: 99%

Neural network based biometric personal identification with fast iris segmentation

Abiyev

Altunkaya

2009

Int. J. Control Autom. Syst.

View full text Add to dashboard Cite

This paper presents the iris recognition system for biometric personal identification using neural network. Personal identification consists of localization of the iris region and generation of a data set of iris images followed by iris pattern recognition. In this paper, a fast algorithm is proposed for the localization of the inner and outer boundaries of the iris region. Located iris is extracted from an eye image, and, after normalization and enhancement, it is represented by a data set. Using this data set a Neural Network (NN) is used for the classification of iris patterns. The adaptive learning strategy is applied for training of the NN. The results of simulations illustrate the effectiveness of the neural system in personal identification.

show abstract

“…These methods use local and global features of the iris. Among many methods used for recognition today, these can be listed: Phase based approach (Daugman,2001;Dougman,2003;Dougman & Downing,1994, Miyazawa et al,2008, wavelet transform, zero crossing approach (Boles & Boashash,1998;S.Avila & S.Reillo, 2002;Noh et al,2002;Mallat,1992), and texture analysis (Wields,1997;Boles & Boashash,1998;Ma et al,2003;Park et al,2003;Ma et al,2005). (Wang & Han, 2003 proposed independent component analysis is for iris recognition.…”

Section: Introductionmentioning

confidence: 99%