Performance evaluation of feature selection methods for ANN based iris recognition

Author

Meetei, Thiyam Churjit ; Begum, Shahin Ara

Author_Institution

Dept. of Comput. Sci., Assam Univ., Silchar, India

fYear

2013

fDate

13-14 Sept. 2013

Firstpage

208

Lastpage

213

Abstract

Iris recognition is receiving increasing attention as a means of personal recognition. Statistical methods, namely Single Value Decomposition (SVD), Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are employed to extract the iris feature from a pattern named IrisPattern based on the iris image. These extracted patterns are classified by using a Feedforward Backpropagation Neural Network (BPNN) with different dimensions of features. From the experimental result, it is observed that ICA is the most appropriate feature extraction method for BPNN for the data sets used.

Keywords

backpropagation; feature extraction; feature selection; feedforward neural nets; image classification; independent component analysis; iris recognition; performance evaluation; principal component analysis; singular value decomposition; ANN; BPNN; ICA; IrisPattern; PCA; SVD; artificial neural network; classification; feature selection methods; feedforward backpropagation neural network; independent component analysis; iris feature extraction; iris recognition; performance evaluation; personal recognition; principal component analysis; single value decomposition; Computed tomography; Feature extraction; Image segmentation; Iris; Iris recognition; Principal component analysis; Vectors; ANN; ICA; Image Segmentation; PCA; SVD;

fLanguage

English

Publisher

ieee

Conference_Titel

Emerging Trends and Applications in Computer Science (ICETACS), 2013 1st International Conference on

Conference_Location

Shillong

Print_ISBN

978-1-4673-5249-9

Type

conf

DOI

10.1109/ICETACS.2013.6691424

Filename

6691424