A novel iris recognition technique using monogenic wavelet phase encoding

Birajadar, Parmeshwar; Shirvalkar, Pushkaraj; Gupta, Shubham; Patidar, Varun; Sharma, Utkarsh; Naik, Ameya K.; Gadre, Vikram M.

doi:10.1109/iconsip.2016.7857494

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…The monogenic wavelets capture the local phase and local orientation orthogonally with respect to the magnitude and hence are suitable for extracting the ridge structures of touchless fingerprints. In our previous work [40], we have proved the ability of capturing local phase with monogenic wavelets compared with Gabor wavelet and Fourier phase by conducting phase-based reconstruction experiments. The main reason of the performance improvement is the effective enhancement of ridge structures of touchless fingerprint images using the proposed enhancement algorithm.…”

Section: Resultsmentioning

confidence: 99%

Towards smartphone-based touchless fingerprint recognition

et al. 2019

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The widely used conventional touch-based fingerprint identification system has drawbacks like the elastic deformation due to nonuniform pressure, fingerprints collection time and hygiene. To overcome these drawbacks, recently the touchless fingerprint technology is gaining popularity and various touchless fingerprint acquisition solutions have been proposed. Nowadays due to the wide use of the smartphone in various biometric applications, smartphone-based touchless fingerprint systems using an embedded camera have been proposed in the literature. These touchless fingerprint images are very different from conventional ink-based and live-scan fingerprints. Due to varying contrast, illumination and magnification, the existing touch-based fingerprint matchers do not perform well while extracting reliable minutiae features. A touchless fingerprint recognition system using a smartphone is proposed in this paper, which incorporates a novel monogenic-wavelet-based algorithm for enhancement of touchless fingerprints using phase congruency features. For the comparative performance analysis of our system, we created a new touchless fingerprint database using the developed android app and this is publicly made available along with its corresponding live-scan images for further research. The experimental results in both verification and identification mode on this database are obtained using three widely used touch-based fingerprint matchers. The results show a significant improvement in Rank-1 accuracy and equal error rate (EER) achieved using the proposed system and the results are comparable to that of the touch-based system.

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Section: Resultsmentioning

confidence: 99%

Towards smartphone-based touchless fingerprint recognition

et al. 2019

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“…While achieving remarkable accuracy in same-spectrum iris recognition in both spectrums (4% EER for NIR and 7% EER for VL), their method falls short in cross-spectral matching and gives relatively poor results (34% EER). The poor performance of 1D log Gabor features in cross-spectral matching, the performance of monogenic wavelets in iris recognition in our previous work [8] and the fact that the VL irises are color images prompted us to employ Color Monogenic Wavelets for the purpose of cross-spectral iris recognition, as we felt by using monogenic color wavelets, we could take advantage of not just the color information in these images which gets lost during the RGB-to-Grayscale conversion, but also the superior local feature extraction ability of monogenic wavelets.…”

Section: Introductionmentioning

confidence: 82%

A Novel Visible Iris Recognition using Color Monogenic Wavelets

Birajadar,

Sharma,

Shete

et al. 2023

SMSJ

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Visible light (VL) iris recognition technology[1] has a growing number of applications in the field of security and citizen identification with the growing number of surveillance cameras and smartphone cameras that acquire iris images in the visible wavelength range. The problem of iris recognition has also led to a growing interest in the development of robust algorithms for visible light iris recognition. Visible light iris recognition is also important for better results on light-colored irises which observe a richer capturing of iris patterns in the visible light range as compared to the Near-Infrared (NIR) range.VL iris images have been shown to have more noise in the form of shadows and reflections and are not as efficient as NIR iris images in capturing iris patterns for dark-colored irises. In this research work, a novel color monogenic wavelength based VL iris feature extraction is performed on widely used VL databases (PolyU[2] and UTIRIS[3]). The results of the proposed algorithm outperform the commonly used algorithms for NIR iris recognition

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