Facial Recognition: Accuracy in Marketing

March 10, 2019

Measurements of accuracy in search and retrieval, image recognition, and tagging human behavior are variable. Results wander from evaluation to evaluation. One can break a text retrieval system by including emojis or explicit cues that  a statement belongs to a specific context. What does “cool” mean?

Recognition of images is equally tricky. Toss in low contrast images with individuals wearing hats, sun glasses, and motorcycle gang style bandanas. How accurate are these systems?

The way to work around the problem is to craft test sets of content or images. The idea is that a well formed test set will provide a level playing field.

The problem is, of course, that life is not a level playing field.

I read summaries of the NIST image recognition tests, the subsequent calls for control by Microsoft (the fading monopolist), and some of the comments about facial recognition (FR) systems having difficulty with certain ethnic groups.

When I read these, I recall the image from a hotel opening, I wonder if today’s systems can ID these individuals “accurately”:


The write up “Why Chinese Companies Plug a US Test for Facial Recognition” summarizes the results of a NIST test and references other FR bake offs. The results are variable.

My view is that the Chinese systems’ performance is less about accuracy and more about sending a message; namely,

China is in the game.

The point of FR may be that accuracy is less important than reminding those interested in FR that Chinese technology has caught up and may surpass US smart software.

Will FR be 100 percent accurate? Not as long as photos like this have to be figured out:


FR is making progress. Progress is incremental. That applies to the US and other countries’ systems.

Stephen E Arnold, March 10, 2019


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