Bachoo, ATapamo, J-R2009-10-212009-10-212009-03Bachoo, A and Tapamo, J-R. 2009. Comparison of features response in texture-based iris segmentation. SAIEE Africa Research Journal, Vol. 100(1), pp 2-111991-1696http://www.saiee.org.za/content.php?pageID=300#1http://hdl.handle.net/10204/3668Copyright: 2009 South African Institute of Electrical Engineers Available online: http://www.saiee.org.za/content.php?pageID=300#1Identification of individuals using iris recognition is an emerging technology. Segmentation of the iris texture from an acquired digital image of the eye is not always accurate - the image contains noise elements such as skin, reflection and eyelashes that corrupt the iris region of interest. An accurate segmentation algorithm must localize and remove these noise components. Texture features are considered in this paper for describing iris and non-iris regions. These regions are classified using the Fisher linear discriminant and the iris region of interest is extracted. Four texture description methods are compared for segmenting iris texture using a region based pattern classification approach: Grey Level Co-occurrence Matrix (GLCM), Discrete Wavelet Transform (DWT), Gabor Filters (GABOR) and Markov Random Fields (MRF). These techniques are evaluated according to their true and false classifications for iris and non-iris pixels.enIrisTexture featuresSegmentationPattern classificationFisher linear discriminantGrey level co-occurrence matrixDiscrete wavelet transformGabor filtersMarkov random fieldsOptronic sensor systemsArticleBachoo, A., & Tapamo, J. (2009). Comparison of features response in texture-based iris segmentation. http://hdl.handle.net/10204/3668Bachoo, A, and J-R Tapamo "Comparison of features response in texture-based iris segmentation." (2009) http://hdl.handle.net/10204/3668Bachoo A, Tapamo J. Comparison of features response in texture-based iris segmentation. 2009; http://hdl.handle.net/10204/3668.TY - Article AU - Bachoo, A AU - Tapamo, J-R AB - Identification of individuals using iris recognition is an emerging technology. Segmentation of the iris texture from an acquired digital image of the eye is not always accurate - the image contains noise elements such as skin, reflection and eyelashes that corrupt the iris region of interest. An accurate segmentation algorithm must localize and remove these noise components. Texture features are considered in this paper for describing iris and non-iris regions. These regions are classified using the Fisher linear discriminant and the iris region of interest is extracted. Four texture description methods are compared for segmenting iris texture using a region based pattern classification approach: Grey Level Co-occurrence Matrix (GLCM), Discrete Wavelet Transform (DWT), Gabor Filters (GABOR) and Markov Random Fields (MRF). These techniques are evaluated according to their true and false classifications for iris and non-iris pixels. DA - 2009-03 DB - ResearchSpace DP - CSIR KW - Iris KW - Texture features KW - Segmentation KW - Pattern classification KW - Fisher linear discriminant KW - Grey level co-occurrence matrix KW - Discrete wavelet transform KW - Gabor filters KW - Markov random fields KW - Optronic sensor systems LK - https://researchspace.csir.co.za PY - 2009 SM - 1991-1696 T1 - Comparison of features response in texture-based iris segmentation TI - Comparison of features response in texture-based iris segmentation UR - http://hdl.handle.net/10204/3668 ER -