#StackBounty: #python #clustering Alternative means of clustering streams of incoming facial recognition data

Bounty: 100

I have a time-series dataset of incoming face data. Each data point is a facial-feature-vector of length 256 that represents the facial features of a person (it is generated by a modified RESNET). Features that are close together are deemed to belong to the same person.

I am (successfully) clustering the incoming face features by DBSCANing. I’ve recently switched to HDBSCAN also with good results.

My problem is this: DBSCAN and HDBSCAN require I have all the data together at one time. I often have >200,000 features which can be a very large download.

I would much prefer to be able to take every incoming f and assign it to a person without having to collect all the information at one time.

Is there an alternative to this (preferable with a Python implementation)?


Get this bounty!!!

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