The purpose of this study is to analyze picture quality using the PreFace software to determine features that could possibly have an affect on the matching of the subjects’ photo in MegaMatcher software. PreFace aids in the extraction of facial features useful for creating SC37/INCITS facial image records and image analysis and detection of compliance with respect to a specific facial image standard. MegaMatcher is a software for reliable and quick identification of biometric features, such as facial recognition. Through the analysis of the data output from the PreFace metrics we identified possible features that may hinder matching performance.

1. IMAGE QUALITY ANALYSIS OF FACIAL FEATURES
The purpose of this study is to analyze picture quality using the PreFace software to determine features that could possibly have an affect on the matching of the
subjects’ photo in MegaMatcher software. PreFace aids in the extraction of facial features useful for creating SC37/INCITS facial image records and image
analysis and detection of compliance with respect to a specific facial image standard. MegaMatcher is a software for reliable and quick identification of biometric
features, such as facial recognition. Through the analysis of the data output from the PreFace metrics we identified possible features that may hinder matching
performance.
Nick Conforti | Eric Daugharty | Brennan Perez | Xing Zhen | Stephen Elliott | Kevin O’Connor
Overview
PreFace Image Quality Analysis
The first thing we had to do is run both sets of images through PreFace to determine the
quality of the images and pick out and features that may have an affect on the matching
of the passport photos to the current photos. We explored the different PreFace output
metrics and their definitions, then began to graph the output metrics categories for each
data set (Shown to the left).
Current Photo Dataset Government ID’s Dataset
PreFace Analysis
Future Work
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Probabilityoffacebeinginimage
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Face Strength – Lab Photo
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ProbabilityofFaceBeinginImage
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Face Strength – ID’S
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PercentBackgroundGray(Valuescanbeintherange
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Percent Background Gray
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Percent Background Gray
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DegreeofBlur(Scoresareintherange0to5.With0
indicatingnoblurand5indicatingahighdegreeof
imageblur)
Image Number
Degree of Blur
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DegreeofBlur(Scoresareintherange0to5.With0
indicatingnoblurand5indicatingahighdegreeof
imageblur)
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Degree of Blur
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Glasses(0indicatesglassesarenotpresent)
Image Number
Glasses Present
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Glasses(0indicatesglassesarenotpresent)
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Glasses Present
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NumberofPixelsBetweenLeftandRightEyeCenters
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Eye Separation
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NumberofPixelsBetweenLeftandRightEyeCenters
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Eye Separation
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DegreeofClutter(Scoresareintherange0to5.With
0indicatingnobackgroundclutterand5indicatinga
highdegreeofbackgroundclutter)
Image Number
Degree of Clutter
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DegreeofClutter(Scoresareintherange0to5.With
0indicatingnobackgroundclutterand5indicatinga
highdegreeofbackgroundclutter)
Image Number
Degree of Clutter
For future work we want to run an analysis of the two data sets in MegaMatcher to
determine the match quality score between the two different image sets. We could then
use that match quality score to better determine different aging features that may or may
not affect the MegaMatcher algorithm.
*Note: We were not able to include subject photo examples due to IRB protocol.
In total we looked at and graphed 19 different PreFace metrics. The metrics shown on
the right side of the poster were determined by our team to possibly be the most
influential in determining weather then lab photo of the subject and government ID would
match.
Image Quality Metrics Analysis
• Face Strength – The face strength metric represents the probability of there being a
face in the image. If a reading here was low it could indicate an image problem and
may result in a subjects images not matching. The lab photos have a higher rating in
general as opposed to the government ID’s meaning the lab photos may be a higher
quality.
• Percent Background Gray – This represents the percent of background gray in the
image. The percent for facial recognition is 18%. The reading from our dataset are
quite high which may cause a problem with MegaMatcher.
• Degree of Blur – This represent the blurriness of a picture rated on a scale from 0 – 5
with 0 being no blur and 5 being blurry. The lab photos again show to be less blurry
then the government ID’s on average.
• Glasses Present – This represents the determination of whether or not there are
glasses present in the picture. This is important because if a subject has glasses on in
one photo and not the other, it may cause a problem with the MegaMatcher quality
score.
• Eye Separation – This represent the number of pixels between the eyes of a subject.
Depending on the distance of the subject to the camera, this number may vary greatly
from the lab photo to the government ID. This could also possibly be problematic is the
difference is large.
• Degree of Clutter – This represents the amount of ‘noise’ or clutter in the background
of the image. The rating is scored on a scale of 0 – 5 with 0 being now background
clutter and 5 being background clutter. This could potentially cause a problem when
MegaMatcher attempts to isolate the face from the images background.
Image Quality Analysis Results
The overall analysis of the two image sets leads us to note that we may see a few
problems with correctly matching images when moving on MegaMatcher due to the
differences in image quality of the two sets. From the PreFace output we have
determined that the lab photos seem to be of higher quality then the government ID’s.
We determined some of the data output was a result of environmental factors such as
lighting, camera quality, and background quality. In addition, we found that there were
other hindrances such as glasses, image glare or blur that may have contributed to poor
data output by the PreFace software.