Iris Recognition Based on Multichannel Gabor Filtering

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A review of biometric technology along with trends and prospects (2014, methodology): “Table 3: Summary of ocular region modalities: Modality Feature Set Recognition Techniques Datasets Iris Color, shape and iris texture (crypts, furrows, corona, freckles) 2D Gabor filters [144, 154, 155], Wavelets [156-159], LoG filter [160], DCT [161], Ordinal measures [162, 163], ICA [164], PCA [16”
Personal Identification Based on Iris Texture Analysis (2003, background): “3, the second image is out of focus, the third one contains many noticeable interlacing lines (especially in regions close to the boundary) caused by eye motion, and the last one is an example of severe occlusions by eyelids and eyelashes.”
Efficient iris recognition by characterizing key local variations (2004, methodology): “Our earlier attempts to iris recognition developed the texture analysis-based methods [14], [17], [18] and a local intensity variation analysis-based method [37].”
Iris recognition using circular symmetric filters (2002, methodology): “From Table 2 and Figure 3, we can see the performance of our new method is much better than that of our previous method [13].”
UBIRIS: A Noisy Iris Image Database (2005, methodology): “This methodology is the most widely used, being proposed with minor several variants in [3], [11], [14], [17], [19], [18] and [20].”