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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.


Example of noise reduction with morphological technique in VASIR.
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f19-jres.118.011: Example of noise reduction with morphological technique in VASIR.

Mentions: The presence of noise in our dataset could be caused by blurriness, eyelashes, eyelids, glasses, reflections, lens distortion, or shadows; the Teh-Chin algorithm does not, however, perform well in such noisy environments [34]. To reduce the noise in an image, VASIR applies a Gaussian filter to the binary threshold image and uses morphological opening and closing later on. Opening of an image is an erosion followed by a dilation to remove small objects from a binary image while preserving the shape and size of larger objects within the image. Closing, on the other hand, consists of dilation followed by an erosion to eliminate small holes and fill gaps in the image [22]. The process of finding an optimal threshold value this way is possibly slower since the optimal threshold is calculated differently depending on the amount of noise within an image. However, we found this method to be much more effective for reducing noise as shown in Fig. 19.


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)

Example of noise reduction with morphological technique in VASIR.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f19-jres.118.011: Example of noise reduction with morphological technique in VASIR.
Mentions: The presence of noise in our dataset could be caused by blurriness, eyelashes, eyelids, glasses, reflections, lens distortion, or shadows; the Teh-Chin algorithm does not, however, perform well in such noisy environments [34]. To reduce the noise in an image, VASIR applies a Gaussian filter to the binary threshold image and uses morphological opening and closing later on. Opening of an image is an erosion followed by a dilation to remove small objects from a binary image while preserving the shape and size of larger objects within the image. Closing, on the other hand, consists of dilation followed by an erosion to eliminate small holes and fill gaps in the image [22]. The process of finding an optimal threshold value this way is possibly slower since the optimal threshold is calculated differently depending on the amount of noise within an image. However, we found this method to be much more effective for reducing noise as shown in Fig. 19.

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.