This paper has the following contributions in iris recognition compass: first, novel parameters selection for Gabor filters to extract the iris features. Second, due to iris textures randomness and assigning the Gabor parameters by pre-knowledgeable values, traditionally, a large Gabor filter bank has been used to prevent losing the discriminative informat ion. It leads to perform extracting and matching the features heavily and on the other hand, the generated feature vectors are lengthened as required for extra storage space. We have proposed and compared two different approaches based on Genetic Algorithm to reduce the system co mplexity: optimizing the Gabor parameters and feature selection. Third, proposing a novel encoding strategy based on the texture variat ions to generate compact iris codes. The experimental results show that generated iris codes by optimizing the Gabor parameters approach is mo re distinctive and compact than ones based on feature selection approach. Since 2003 he has been on the faculty of the Depart ment of Teleco mmunication Eng ineering, Shiraz University of Technology, Shiraz, Iran. His activities have included Image signal processing, digital filter structures, filter banks, wavelet based signal processing and wireless communication. He is a Member of the IEEE.Habi bol ah Danyali received the B.Sc. and M.Sc. degrees in Electrical Engineering respectively fro m the
Deep learning is a rapidly growing discipline that models high-level features in data as multilayered neural networks. In this paper, we propose a deep learning approach for 3D shape retrieval using a multi-level feature learning methodology. We first extract low-level features or local descriptors from a 3D shape using spectral graph wavelets. Then, we construct mid-level features from these local descriptors via the bag-of-features paradigm by employing locality-constrained linear coding as a feature coding method, together with the biharmonic distance as a measure of the spatial relationship between each pair of bag-of-feature descriptors. Finally, high-level shape features are learned via a deep auto-encoder, resulting in a deep shape-aware descriptor that is compact, geometrically informative and efficient to compute. The proposed 3D shape retrieval approach is evaluated on SHREC-2014 and SHREC-2015 datasets through extensive experiments, and the results show compelling superiority of our approach over the state-of-the-art methods.
Iris reputes for its potential to identify the people with high accuracy in large scale. This is not achieved unless the iris patterns are well represented. Gabor filtering is vastly used in iris recognition literature for feature extraction. Conventionally, Gabor parameters value are supplied by pre-knowledgeable values so that the filter bank size is increased to prevent the losing information. In this paper, multi objective genetic algorithm (MOGA) is used to optimize the Gabor-wavelet in order to reduce the filter requirements and increasing the accuracy. The feature vectors are encoded by phase quantization and a novel method based on iris texture variation. Experimental results show recognizing with CRR=99.68% and EER=0.26% for codes with length only 496 bits on a subset including 2125 iris images from CASIA-IrisV3-Interval database.
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