Micro Expressions are Getting the Machine Learning Treatment

It Knows How You Feel

A few months I ago I started this site with an article on intent detection. In it I made the argument that protecting us from technology by banning it is intractable, so it’s probably more beneficial to focus on detecting humans who are about to carry out malicious acts.

One of the tools I proposed for achieving intent detection is the application of machine learning to detect micro expressions – those quick, involuntary displays of emotion we do but have little to no control over.

It is fascinating watching a field develop before your very eyes. Recently, a paper was published to arXiv on the application of machine learning to the detection of micro expressions. MIT Technology Review has also written a summary of the paper.

So, have these researchers solved the problem? Not yet, but as it goes with most computer science, their work sets a new bar for “state of the art.” To paraphrase, the advances they have made are:

1) They developed a new method to detect when a micro expression occurs during a video that does not require prior training. This is important because in order to recognize something, most computer vision systems need to be told where it is in the first place. The authors use the terms “spotting” and “recognizing” to distinguish between the two. Since their method does not require training, it makes the process of acquiring a large sample of micro expressions to work with easier. This is probably the most important achievement.

2) They utilized a new method to amplify the distinguishing features of a micro expression to make it more recognizable. This is important because micro expressions are generally very subtle, which makes them harder to classify.

3) They investigated multiple methods to mathematically represent the features needed to distinguish micro expressions, and analyzed their relative performances. This is important because the feature representation that works best on say, a color video does not work as well with near-infrared videos. Also (somewhat obviously), using a high speed camera makes it easier to recognize micro expressions.

4) They created an automatic recognition system that can detect and classify micro expressions from long videos with performance comparable to humans.

For those into machine learning, they performed the actual classification task with a linear Support Vector Machine. No fancy deep learning or neural networks, just good old large margin classification with customized feature descriptions.

It will be interesting to watch the field evolve over the next few years as researchers start applying the advancements of deep learning to the problem. We’re getting ever closer to viable intent detection.

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