We analyzed the following scalars and their phase features:

*centx*:*x*coordinate of centroid of moving region*wcentx*:*x*coordinate of centroid of moving region weighted by |(*u,v*)|*wcenty*:*y*coordinate of centroid of moving region weighted by |(*u,v*)|*dcentx*:*wcentx - centx**dcenty*:*wcenty - centy**aspct*: aspect ratio of moving region*waspct*: aspect of ratio of moving region weighted by |(*u,v*)|*daspct*:*aspct - waspct**uwcentx*:*x*coordinate of centroid of moving region weighted by |*u*|*uwcenty*:*y*coordinate of centroid of moving region weighted by |*u*|*vwcentx*:*x*coordinate of centroid of moving region weighted by |*v*|*vwcenty*:*y*coordinate of centroid of moving region weighted by |*v*|

Analysis of variance, including *post hoc* testing,
indicated the following:

- All features showed significant variations between people.
- The features showing the greatest variation were
*aspct*,*dcenty*,*wcenty*, and*vwcenty*.

The following scatter plots show the features with the greatest
variation, *centx*, *wcentx*, and *aspct*:

We tested recognition using a variety of algorithms.
The best results were obtained by finding the nearest neighbor from
a set of exemplars, vectors of mean feature values
for each subject.
To get an unbiased estimate of the recognition rate we used a
leave-one-out procedure. Using the full feature vector gives
a recognition rate of about 90%. If we use only the four best features,
*daspct*, *dcenty*, *vwcenty*, and *waspct*, then
the recognition rate went as high as 95%. In comparison, recognition by
chance would yield a rate of about 17%.

Recognition was possible for a variety of flow sources varying in spatial resolution. We observed that while the exemplars changed and the features with the greatest variation changed, as long as the parameters for flow computation were kept constant, recognition is successful.

Analysis of variance and our recognition test show that the
features have the following *approximate* significance for
recognition:

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