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This is a system that correlates microexpressions across
videos of interviews. It would correlate individual frames
of microexpressions on the faces of interview
across time. The best way to start would probably be
a group of well known people who all know eachother
and of whom there are many close up videos of
and then correlate the microexpressions statistically to
if there is, and then map the history of exchange over
The theory would be that people copy emotions from
eachother. In my own personal experience of watching
myself on video I have noticed that many individual
look unlike me and very much like people I have known
my past who have had emotional influences on me. If
could correlate this statistically you could get a better
picture of how "thoughts" really happen, that they are
original in the way we are conscious of now but are
traded around in a relatively unconscious economy.
Ian Tindale recites EmotionML 5.1.3 Generation of emotion-related system behaviour
Ian Tindale recites the passages of prose in http://www.w3.org/TR/emotionml/#s5.1.3 EmotionML 5.1.3 Generation of emotion-related system behaviour: Generation of facial expressions in an MPEG-4 face model. [Ian Tindale, Jul 24 2015]
||I'd probably start with words rather than
microexpressions. Words often correlate more or less
with what people are thinking.
||Your naivety is touching.
||Thank you. I didn't get where I am today.
||Ah! So _that's_ where it went! I'll call [8th] - time to
test out Tactical Tapas No. 74.