Steerable Riesz wavelet based approach for iris recognition

Abstract

In this paper, we explore the applicability of first and second order monogenic Steerable Riesz wavelet components for iris recognition. These wavelets provide powerful mechanism to extract the invariant as well as covariant local variations of iris patterns. Unlike other existing methods where sole iris (either left or right) is used for recognition, in our work, we extract the features from both left and right irises, encode them separately and perform bit level fusion. Extensive experimentation has been conducted on the benchmark databases namely, IITD, MMU v-2 and CA-SIA v-4 distance to exhibit the performance of the proposed method. Comparative analysis is also performed with the state of the art methods to justify the suitability of the proposed method for iris recognition.

Multi-Patches Iris Based Person Authentication System Using Particle Swarm Optimization and Fuzzy C-Means Clustering

“…We have experimented the proposed segmentation method and multi-patch technique on the IITD, MMU v-2 and CASIA v-4 distance databases using the feature extraction techniques explained in section 3.3. Experiments are conducted using bit level fusion technology given in (Shekar and Bhat, 2015). Recognition rates obtained on the three databases applying the filters LogGabor, Riesz and TSE and their combinations are presented in Table 3.…”

“…We have used Log-Gabor filter given by Masek (Masek and Kovesi, 2003), Riesz filter (Shekar and Bhat, 2015) and Taylor series expansion (TSE) filter (Shekar and Bhat, 2016) and the combinations of these filters and have compared the results. A brief introduction of these filters is given below.…”

Brain Signals as a New Biometric Authentication Method Using Brain-Computer Interface

“…In iris identification, the probability of error is the lowest of all biometrics. (Shekar and Bhat 2015). Table 4 shows some specific advantages and disadvantages of this biometric technique ( Biometrictoday).…”

Reliability Index for Iris Code Matching in Iris Recognition Systems

“…For iris segmentation we have adopted the method proposed by (Proenca, 2010). In order to extract the iris features, we have adopted the filters, DCT (Monro et al, 2007), Log-Gabor (Masek et al, 2003), Riesz (Shekar and Bhat, 2015) and TSE (Shekar and Bhat, 2016). Daugman's rubber sheet model and phase encoding techniques are used for unwrapping the iris and encoding the features respectively.…”