What Is Better Than Biometrics Emotion Analysis of Surveillance Videos?

October 27, 2022

Many years ago, my team worked on a project to parse messages, determine if a text message was positive or negative, and flag the negative ones. Then of those negative messages, our job was to rank the negative messages in a league table. The team involved professionals in my lab in rural Kentucky, some whiz kids in big universities, a handful of academic experts, and some memorable wizards located offshore. (I have some memories, but, alas, these are not suitable for this write up.)

We used the most recent mechanisms to fiddle information from humanoid outputs. Despite the age of some numerical recipes, we used the latest and greatest. What surprised everyone is that our approach worked, particularly for the league table of the most negative messages. After reviewing our data, we formulated a simple, speedy way to pinpoint the messages which required immediate inspection by a person.

What was our solution for the deployable system?

Did we rely on natural language processing? Nope.

Did we rely on good old Reverend Bayes? Nope.

Did we rely on statistical analysis? Nope.

How did we do this? (Now keep in mind this was more than 15 years ago.)

We used a look up table of keywords.

Why? It delivered the league table of the most negative messages more than 85 percent of the time. The lookups were orders of magnitude faster than the fancy numerical recipes. The system was explainable. The method was extensible to second order negative messages with synonym expansion and, in effect, a second pass on the non-really negative messages. Yep, we crept into the 90 percent range.

I thought about this work for a company which went the way of most lavishly funded wild and crazy start ups from the go to years when I read “U.K. Watchdog Issues First of Its Kind Warning Against ‘Immature’ Emotional Analysis Tech.” This article addresses fancy methods for parsing images and other content to determine if a person is happy or sad. In reality, the purpose of these systems for some professional groups is to identify a potential bad actor before that individual creates content for the “if it bleeds, it leads” new organizations.

The article states:

The Information Commissioner’s Office, Britain’s top privacy watchdog, issued a searing warning to companies against using so-called “emotional analysis” tech, arguing it’s still “immature” and that the risks associated with it far outweigh any potential benefits.

You should read the full article to get the juicy details. Remember the text approach required one level of technology. We used a look up table because the magical methods were too expensive and too time consuming when measured against what was needed: Reasonable accuracy.

Taking videos and images, processing them, and determining if the individual in the image is a good actor or a bad actor, a happy actor or a sad actor, a nut job actor or a relative of Mother Teresa’s is another kettle of code.

Let’s go back to the question which is the title of this blog post: What Is Better Than Biometrics Emotion Analysis?

The answer is objective data about the clicks, dwell time, and types of indexed content an individual consumes. Lots of clicks translates to a signal of interest. Dwell time indicates attention. Cross correlate these data with other available information from primary sources and one can pinpoint some factoids that are useful in “knowing” about an individual.

My interest in the article was not the source article’s reminder that expectations for a technology are usually over inflated. My reaction was, “Imagine how useful TikTok data would be in identify individuals with specific predilections, mood changes plotted over time, and high value signals about an individual’s interests.”

Yep, just a reminder that TikTok is in a much better place when it comes to individual analysis than relying on some complicated methods which don’t work very well.

Practical is better.

Stephen E Arnold, October 27, 2022


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