he latest batch of experiments looked at how one might cope with a mask closer than usual to the eye’s centre. I used harder pairs.

It did not work too well. One remaining limitation is that in an attempt to determine fiducial-esque points based on unmarked (not annotated) 3-D data there is little other than the nose tip that can consistently and accurately be pinned down. The eyes in particular are not simple to segment in 3-D — not without some help from 2-D anyway — mostly because the corners are fuzzy in 3-D. While slight head rotations can be annulled with ICP, there is still a difficulty associated with true distances as measured based on the range images. Accurately-measured geodesic distances are supposed to be robust to that, but in practice when there is slight rotation difference some of the calculations don’t add up. The sensitivity to inherent differences is often outweighed by pose.

Classification mistakes were partly caused by hair, occlusion, and other factors. I have rerun the same experiments as before with twice the number of vertices, but unsurprisingly, the results were about the same. The excessive detail gains never brought much improvement in terms of verification performance. Next, I took the best approach of the bunch and ran it on some of the hardest cases. The ROC curve is shown while for simpler cases the experiment is still being run on two servers.

There’s still no hope of beating state-of-the-art performance levels, unless of course a significantly improved variant is found.

Some of the problems are easier for the human eye to see, such as cases where hair penalises the scoring mechanism, as shown in the picture below.

Random points were then attempted, adding a stochastic nature to this problem. In order to make GMDS ‘fail’ most badly only in the case of false pairs, I have tested some new masks and measured verification performance reached by using them. For GMDS it failed quite badly, but with the other FMM-based approach — applied to some hard cases — I got the results shown in the ROC curve. Rather than dilating the masks and varying the hole sizes I would like to try varying positions from which to dilate in order to sample more distinct regions and measure distances upon those. This seems like an approach with real potential, provided the random (or fixed) sample of points is large enough to compensate for noise/intra-person variation. The latest experiment was preparatory towards this approach.

Random positions were further tested by making a variation, an improvement to the above. By letting the anchor points move around a bit (randomly within range) I was unable to get better performance than before (just over 90% verification rate on hard cases too). There are other methods that I could try next…

120 random positions were then placed on pairs to further test the approach. These further attempts to improve performance by moving points randomly (and this time taking a larger random sample) were not quite so successful. The general premise was, by taking many points around the face and expanding from them (with geodesic means) will lead to a good and rather unique signature. In practice, however, the measure is insensitive to real anatomical differences. Intra-person differences can outweigh inter-person differences. I’ll try another approach, but it will take days for results to arrive.

Although this is being tested on faces at the moment, the methods are generalisable and can be applied to any biomedical data for similar purposes.

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