IRHDF: Iris Recognition using Hybrid Domain Features

Arunalatha, J S; Rangaswamy, Y; Shaila, K; Raja, K B; Anvekar, Dinesh; Venugopal, K R; Iyengar, S. Sitharama; Patnaik, L. M.

doi:10.1109/indicon.2015.7443807

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…The retrieval accuracy of CBIR is enhanced by taking into account microscopic and directional information. Arunalatha et al, [20] presented Iris Recognition using Hybrid Domain Features (IRHDF). The frequency and spatial image features are extracted and combined to obtain a feature set, then similarity match is computed using Euclidean Distance for the iris detection.…”

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confidence: 99%

Soft Computing based Artificial Neural Network and Multiple Feature set Intelligence system for Image Retrieval

Arunalatha

Vasanthakumar

et al. 2022

Preprint

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This paper proposes an Image Retrieval model using Multiple Feature Sets and Artificial Neural Network (IR-MFS-ANN), where the multiple features Histogram of oriented Gradient (HoG), Overlapping Local Binary Pattern (OLBP), Color and Statistical features are considered. Visual information is one of the most important data in the field of social networking, medicine, military, and these areas contain an enormous volume of semi-organized and organized heterogeneous information related to explicit subjects. However, retrieval and usage of suitable information from the comprehensive information archives are important to meet the content extraction and retrieval challenges. To improve retrieval performance, the image representation, modeling, scalable algorithm that permit accessing large archives are integrated into the retrieval framework. The proposed model utilizes ANN to find the distance between feature vectors. The proposed algorithm is tested and analyzed with various retrieval techniques and it is found that ANN-based image retrieval outperforms the state-of-the-art techniques [1–5]. The proposed method results in accuracy of 94%, 92%, 95%, and 94% for Wang, Cifar-10, Oxford Flower, and ImageNet standard databases respectively.

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confidence: 99%