Rebuild Faces recognition database without losing Face associations

Does anybody know a safe way to rebuild a Faces recognition database without:
1) disturbing already associated faces / pictures
2) finding new faces
I've already done a huge amount of manual editing and auto-recognition on my 40K photo database and have culled many of the "don't care" faces and hand-identified literally thousands of faces taht reconitions didn't identify.  Unfortunately the recognition database appears to be corrupted, manifiesting itself in two ways:
1) Count of suggested recognized faces doesn't match the number iPhoto claims to be there, plus several of the "suggestions" shown have already been previously Confirmed.
2) Crashes that seem to be related to memory leaks - iPhoto use of memory piles up until it rolls over 1.7G on a machine with 4GB RAM, and now over 3.8GB on a machine with 8GB RAM, then crashes.
I'm hoping there is a way to rebuild recognition database based on tens of thousands meticulously identified, and seemingly accurate (no corruption), face associations.  Thoughts ?

Thanks Terence,
Was hoping for some miracle, but your thoughts make sense.  I'm actually pursuing a 3-fold recovery strategy and will document what works best.
1) Keep going with the current database despite issues. So far, recognition database corruption seems limited to particular faces that have huge numbers of the same person (3-7K), not smaller lists. And I don't appear to be getting cross corruption where I label a person, then they come back with a different name. Only the suggestion lists appear to have problems, and again, only for a few select people. I also upgraded to 8G of memory (vs. 4G) which seems to give me more running room to avoid/postpone crashes from memory leaks.  iPhoto databases is still incredibly valuable given all the hand work.  When it comes time to add faces I can still get recognition to work more quickly and without weirdness via two methods:
* Start a new Faces entry, with a slightly different name, for a person in one of the "oversized/corrupted" Face entries. Gather up enough faces manually to support reconition, then run recognition for the new entry.  Once you have collected/gleened as many faces as one can, merge the entry into the existing, ovesized Face entry.
* and bring any new photos/events in via iPhoto Manager from a clean iPhoto database (see next).  Faces come over from the clean database.
2) Develop a new recognition database.  I have two physically different iPhoto databases (both Time Machined) that I keep on separate drives. I keep them synced via iPhoto Manager. I recently removed all the "faces" database files from the "shadow iPhoto database" and have begun rebuilding.  I now use that as my master for adding/importing new photos and runnign Faces (though Photostream makes that a little trickier).  When new events are the way I like, I copy over to the old "corrupt" database using iPhoto manager.  Working nicely, but I'm now begining to realize the scope of the naming I did in the "corrupt" database - hundreds of hours to get back to where I am with corrupt database.
3) I'm also trying Picasa recognition on an "in-place" iPhoto database.  Of course, there is no way to transfer back to iPhoto, but a fun experiment in recognition nonetheless. Picasa's recognition is similar in concept but works substantially differently than iPhoto.  A few things I've noticed:
- Picasa does a kind of clustering of very simialr faces that makes it very fast in some recognition steps.  instead of labeling one photo, you label clusters.
- Picasa operates regularly on the whole list of unknown photos, instead of focusing in on recognizing just one person at a time, so one can very quickly wade through confirmation of many people.
- Picasa seems less sensitive to one or two "wrong" persons in a long list of recognized faces vs. iPhoto. Even one wrong photo amonst hundreds appears to steer the iPhoto reconition to pick up many of the wrong person for the suggestion list, while Picasa appears more immune (I would love a utility to weed out a few offending, wrong faces in an already recognized database).
- At the same time, iPhoto appears to go to greater depth in finding all images of a select person when it concentrates on that person.
- Ultimately, both recognizers seem to suffer from "fatigue" and randomness - they get to a poitn where they can recognize now more, until some new random event comes along that frees up or catalyzes additional recognition.
More later

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