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This student is giving a fake smile because he met a strict teacher he hates so much.
We often smile to express our happiness; however, smile is sometimes a mask people frequently employed to hide their pains. The person in the picture above is a student who is giving a fake smile because he met a strict teacher he hates so much.
In Episode 4, let’s compare the analysis performed by two different Artificial Intelligence platforms: Microsoft Azure and M.
Microsoft Azure’s algorithm interprets human emotion by detecting facial muscle movements. In this example, Microsoft Azure mistakenly reads 99.99% happiness from the student. Microsoft Azure’s misinterpretation could be led by seemingly smiling face from the student: the lift at the corners of his mouth and the crescent-shaped eyelids.
M’s algorithm, which identifies human emotions based on hidden and micro facial expressions, accurately reads 76% Disgust, 9.4% Contempt, and 7% Sadness from the student’s face. Accurately identifying human emotions is extremely important in monitoring the mental conditions of patients suffering from mental illness and preventing suicides.
We, at Project M, believe the ability of emotional artificial intelligence platform to identify and understand obvious negative emotions has great potential to optimize humans’ emotional well-being.
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*As of February 2019, the Project M team has devoted 54,000 hours to the AI platform, M. The sample input data we are using is the pure testing data that is completely new to M and Microsoft (assumed) and is never been used to train M, so this comparison is a fair trial between M and Microsoft. We appreciate Microsoft, the leader of the industry and emotional AI sector, for allowing the general public to test their testing site for identifying human emotions based on facial expressions.
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