AST March 2018 Magazine March 2018 Final -3.3.18 | Page 14

Volume 21
March 2018 Edition
• An initial step in this process is image analysis.
• A vetting process must be established by the agency where a
system user analyzes and physically inspects the images for
quality and clarity before conducting any type of facial recog-
nition search or comparison.
• The user must affirmatively answer the questions:
• Does this image meet the criteria for a facial recognition
search?
• Can this image be enhanced by software, or will this image be
rejected?
These purely human expert judgments should be a prerequisite to
starting the process since no facial recognition search or analysis is
ever the same.
Recommended Best Practices for Your Facial
Recognition Investigative Workflow to Pre-
vent Facial Recognition Misuse and Bias • As search results go, facial recognition algorithms read each
face differently.
• When images are higher quality, the process is seamless:
import a face and the software will find a result in the list of
returns, usually returning a facial match with a higher confi-
dence ranking.
• Many facial recognition systems on the market today do this
very well.
Combat False Narratives with These Investigative
Processes What about facial recognition results using images
of lower quality?
Capturing Good Probe Images, Enhancing Lower Quality Images, Facial Analysis,
Two-Tier Verifications and Identifying Your Possible Match
Facial recognition is a proven technology that provides great
and growing value to public safety.
But, I will also firmly state that law enforcement users of
facial recognition technology must live by a clearly defined
process and rock-solid policies that are rigorously exercised
and audited.
If we don’t actively demonstrate the accountability we all
live by, we create open space for fear mongers to air their
falsehoods.
Goal of Facial Recognition to Generate Investigative
Leads
Let’s consider the facts. The goal of using facial recognition technol-
ogy is to generate a strong investigative lead – not to definitively
conclude that a face matches an identity.
• As a former detective who has analyzed thousands of images
for criminal investigations, I can attest that every image intro-
duced into a facial recognition system is unique.
• Because of this, a standard workflow and process must be
established by every agency that uses the technology.
• Assuming these images can be enhanced by facial recognition
software, they can be searched, but confidence rankings in the
gallery of returned faces will vary and the returns will not be
so obvious.
• What is important for agencies to remember is that you can
still leverage a lower quality image to find possible matches.
• You can enhance the image with software and utilize data
filters.
• Search results often change when metadata filters are applied,
especially when searching against a database in the millions,
thousands, or hundreds.
• When search parameters are defined and levels of specificity
are set by the user, the results vary but also become more
precise.
• Users can either narrow or expand searches based on filters
and gallery sizes.
• These factors can improve results, but they still require the
facial recognition user to individually examine each image.
• The algorithms may pick up minute facial features, but the
subject will more likely return deeper in a candidate list.
• The point is that with the right gallery size and appropriate
metadata selections, lower quality images can be used by
facial recognition systems to return results.
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