Iris Recognition by Learning Fragile Bits on Multi-patches using Monogenic Riesz Signals
Unconstrained, at-a-distance iris recognition systems endure the problem of fragile bits. Existence of fragile bits in candidate iris results into low recognition rates. Proposed approach utilizes fragile-bit information for classification of iris bits as consistent or inconsistent. We divide the candidate iris into patches and propose monogenic signals of Riesz wavelets to learn fragile bits in each patch. We propose a new feature descriptor, Riesz signal based binary pattern (RSBP) to extract the features from these patches. Each patch is assigned with a weight pivoted on the fragile bits present in it. Dissimilarity score between the two irises is obtained by adopting weighted mean Euclidean distance (WMED). Experiments are conducted using both near infra red (NIR) and visible wavelength (VW) images, obtained from the benchmark databases IITD, MMU v-2, CASIA-IrisV4-Distance and UBIRIS v2. Results justify the applicability of proposed approach for iris recognition.
KeywordsIris recognition Fragile bits Multi-patches Monogenic signal
This work is supported jointly by the Department of Science and Technology, Government of India and Russian Foundation for Basic Research, Russian Federation under the grant No. INT/RUS/RFBR/P-248.
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