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Iris Features Extraction Using Beamlets and Wedgelets

Rachubiński M.

Machine Graphics and Vision

2009

Vol. 18, No. 3

289-304

A new approach to iris feature extraction using geometrical wavelets is presented. Iris code is generated by using representation of the wavelet coefficients based on a wedgelet dictionary. The accuracy of identification in the case of head inclination by a certain angle for different ranges of possibilities of shifting the iris code is shown. Experimental results on the CASIA iris database show that the proposed method is effective and exhibits encouraging performance.

Intrinsic Dimensional Selective Operator Based on Geometrical Wavelets

Lisowska A.

Journal of Applied Computer Science

2005

Vol. 13, nr 1

99-112

The theory of geometrical wavelets is new and has found already many applications in different fields of digital image processing, though finding many others is still possible and justified. Although images are two dimensional objects they include areas which have different intrinsic (local) dimensionality. Ones of them are more important in the visual perception while the others are less important. The main problem lies in constructing good extractors, which can efficiently extract intrinsic two dimensional areas hidden in images (that is the more important ones). There are some well known nonlinear techniques which can do it relatively effectively. For example the one based on spectral methods, or especially on Volterra series or the one basing on tensors. In this paper there is presented the very novel approach of extracting of intrinsic two dimensional areas based on the theory of geometrical wavelets, especially beamlets. Basing on them the new intrinsic dimensional selective operator has been defined. As performed experiments have shown, giving quite satisfactory results, this approach may constitute serious competition for the well known methods used so far in the theory of intrinsic dimensional operators.