Multi scale multi directional shear operator for personal recognition using Conjunctival vasculature

Tankasala, Sriram Pavan; Doynov, Plamen

doi:10.1109/ths.2015.7225292

Cited by 3 publications

(3 citation statements)

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“…Previous studies on OSV recognition using the CIBIT‐I dataset have used wavelets [21], cross‐correlation of registered eye images [26], GLCM [11], and shearlets [22]. This study provides a best EER of 0.20% using CIBIT‐I dataset, which is better when compared with previous studies that use the same dataset (see Table 8).…”

Section: Comparative Analysismentioning

confidence: 81%

“…Crihalmeanu et al [20] used multispectral imaging in combination with Speeded Up Robust Features (SURF), pixel-topixel correlation, and minutiae points for feature extraction. In our previous work, we used wavelet packet energies [21], contourlets [10], shearlets [22], and the grey level co-occurrence method (GLCM) [11] for OSV feature extraction. Oh et al [23] used local binary pattern features for OSV recognition.…”

Section: Reported Osv Feature Extraction Schemesmentioning

confidence: 99%

“…In this study, using curvelets, an EER of 0.01% was obtained for the same scenario. Table 8 Comparative results from previous studies and proposed curvelets using CIBIT-I dataset Wavelets+NN [19] Cross-correlation of registered images [26] GLCM+NN+FLDA [11] Shearlet+NN [22] Proposed method (curvelets) EER, % 4.30 ∼25.0 11.9 0.29 0.20 Table 9 Reported results for OSV recognition using UBIRIS v1 dataset, listing features extraction and their corresponding recognition rates (EER %) with comment section indicating important evaluation details in the respective works Authors Feature extraction method EER, % Comments…”

Section: Comparisons Using Cibit-ii Datasetmentioning

confidence: 99%

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Ocular surface vasculature recognition using curvelet transform

et al. 2016

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The vascular patterns seen on the white of the eye, mainly in conjunctival and episcleral layers, are termed as ocular surface vasculature (OSV). OSV is visible in images captured with commercial RGB cameras, and its unique texture can be used for biometric recognition. This study demonstrates the capabilities of curvelet transform for OSV feature extraction. Nonlinear feature enhancement and feature mapping in curvelet domain are shown to be effective in differentiating OSV texture. Linear discriminant analysis and similarity metrics are used for matching. A match-score level fusion is used across multiple gaze directions for both eyes. Using a multi-distance dataset of 50 volunteers, where eye images were acquired from 30, 150, and 250 cm using a dSLR, a best equal error rate (EER) of 0.2% is obtained. Using a second dataset of 40 volunteers acquired from 150 cm using a dSLR, a best EER of 3.1% is obtained. For a 216-participant dataset of ocular images acquired using cellular phones from close proximity, an EER of 0.9% is obtained. The proposed methodology was also tested on the publicly available UBIRIS V1 dataset, yielding an EER of 0.7%. The experimental results support the theoretically formulated advantages of the curvelet transform and its capability in successful extraction of curved structures when applied to OSV patterns.

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Section: Comparative Analysismentioning

confidence: 81%