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VASIR: An Open-Source Research Platform for Advanced Iris Recognition Technologies.

Lee Y, Micheals RJ, Filliben JJ, Phillips PJ - J Res Natl Inst Stand Technol (2013)

Bottom Line: VASIR has three primary modules: 1) Image Acquisition 2) Video Processing, and 3) Iris Recognition.Each module consists of several sub-components that have been optimized by use of rigorous orthogonal experiment design and analysis techniques.The results showed that even though VASIR was primarily developed and optimized for the less-constrained video case, it still achieved high verification rates for the traditional still-image case.

View Article: PubMed Central - PubMed

Affiliation: National Institute of Standards and Technology, Gaithersburg, MD 20899.

ABSTRACT
The performance of iris recognition systems is frequently affected by input image quality, which in turn is vulnerable to less-than-optimal conditions due to illuminations, environments, and subject characteristics (e.g., distance, movement, face/body visibility, blinking, etc.). VASIR (Video-based Automatic System for Iris Recognition) is a state-of-the-art NIST-developed iris recognition software platform designed to systematically address these vulnerabilities. We developed VASIR as a research tool that will not only provide a reference (to assess the relative performance of alternative algorithms) for the biometrics community, but will also advance (via this new emerging iris recognition paradigm) NIST's measurement mission. VASIR is designed to accommodate both ideal (e.g., classical still images) and less-than-ideal images (e.g., face-visible videos). VASIR has three primary modules: 1) Image Acquisition 2) Video Processing, and 3) Iris Recognition. Each module consists of several sub-components that have been optimized by use of rigorous orthogonal experiment design and analysis techniques. We evaluated VASIR performance using the MBGC (Multiple Biometric Grand Challenge) NIR (Near-Infrared) face-visible video dataset and the ICE (Iris Challenge Evaluation) 2005 still-based dataset. The results showed that even though VASIR was primarily developed and optimized for the less-constrained video case, it still achieved high verification rates for the traditional still-image case. For this reason, VASIR may be used as an effective baseline for the biometrics community to evaluate their algorithm performance, and thus serves as a valuable research platform.

No MeSH data available.


Related in: MedlinePlus

Comparison of (a) Ideal segmentation (b) IrisBEE’s approach and (c) VASIR’s approach.
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f24-jres.118.011: Comparison of (a) Ideal segmentation (b) IrisBEE’s approach and (c) VASIR’s approach.

Mentions: The blue circles indicate the segmentation results for IrisBEE and the orange diamonds the ones for VASIR. Note that for each feature (pupil, iris, eyelid, and total) on the horizontal axis, the VASIR successful segmentation rate is uniformly higher than the IrisBEE success rate. For both ABIS (Automated Best Image Selection) and HBIS (Human Best Image Selection), VASIR has a total 31 % higher successful segmentation rate than IrisBEE. Particularly, significant is that the VASIR segmentation method leads to an enhancement (32.3 %) of the eyelid detection compared to IrisBEE’s algorithm. Figure 24 illustrates the improvement in segmentation quality between VASIR and IrisBEE.


VASIR: An Open-Source Research Platform for Advanced Iris Recognition Technologies.

Lee Y, Micheals RJ, Filliben JJ, Phillips PJ - J Res Natl Inst Stand Technol (2013)

Comparison of (a) Ideal segmentation (b) IrisBEE’s approach and (c) VASIR’s approach.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4487315&req=5

f24-jres.118.011: Comparison of (a) Ideal segmentation (b) IrisBEE’s approach and (c) VASIR’s approach.
Mentions: The blue circles indicate the segmentation results for IrisBEE and the orange diamonds the ones for VASIR. Note that for each feature (pupil, iris, eyelid, and total) on the horizontal axis, the VASIR successful segmentation rate is uniformly higher than the IrisBEE success rate. For both ABIS (Automated Best Image Selection) and HBIS (Human Best Image Selection), VASIR has a total 31 % higher successful segmentation rate than IrisBEE. Particularly, significant is that the VASIR segmentation method leads to an enhancement (32.3 %) of the eyelid detection compared to IrisBEE’s algorithm. Figure 24 illustrates the improvement in segmentation quality between VASIR and IrisBEE.

Bottom Line: VASIR has three primary modules: 1) Image Acquisition 2) Video Processing, and 3) Iris Recognition.Each module consists of several sub-components that have been optimized by use of rigorous orthogonal experiment design and analysis techniques.The results showed that even though VASIR was primarily developed and optimized for the less-constrained video case, it still achieved high verification rates for the traditional still-image case.

View Article: PubMed Central - PubMed

Affiliation: National Institute of Standards and Technology, Gaithersburg, MD 20899.

ABSTRACT
The performance of iris recognition systems is frequently affected by input image quality, which in turn is vulnerable to less-than-optimal conditions due to illuminations, environments, and subject characteristics (e.g., distance, movement, face/body visibility, blinking, etc.). VASIR (Video-based Automatic System for Iris Recognition) is a state-of-the-art NIST-developed iris recognition software platform designed to systematically address these vulnerabilities. We developed VASIR as a research tool that will not only provide a reference (to assess the relative performance of alternative algorithms) for the biometrics community, but will also advance (via this new emerging iris recognition paradigm) NIST's measurement mission. VASIR is designed to accommodate both ideal (e.g., classical still images) and less-than-ideal images (e.g., face-visible videos). VASIR has three primary modules: 1) Image Acquisition 2) Video Processing, and 3) Iris Recognition. Each module consists of several sub-components that have been optimized by use of rigorous orthogonal experiment design and analysis techniques. We evaluated VASIR performance using the MBGC (Multiple Biometric Grand Challenge) NIR (Near-Infrared) face-visible video dataset and the ICE (Iris Challenge Evaluation) 2005 still-based dataset. The results showed that even though VASIR was primarily developed and optimized for the less-constrained video case, it still achieved high verification rates for the traditional still-image case. For this reason, VASIR may be used as an effective baseline for the biometrics community to evaluate their algorithm performance, and thus serves as a valuable research platform.

No MeSH data available.


Related in: MedlinePlus