I purchased Lightroom 6 about 4 hours after it was released on April 21st and my computer had been indexing faces from that time almost none to about 4pm Monday May 3rd, so about 13 days later it finished. This was processing 228,000 images, guessing about half had faces in them.
One of the big things Lightroom 6 supposedly brought to the table over Lightroom 5 was performance improvements, but I have not yet seen any evidence of it, but more to the contrary. Though to be honest.. I have been working off my laptop for the last two weeks because my desktop LIGHTROOM (not the computer itself) was so locked up doing facial recognition it was otherwise unusable. So if there are performance improvements in other modules I haven’t particularly noticed too many of them yet.
Thanks to a previous post commenter (JBurroughs) for pointing out that you do not need to be in Facial Recognition mode for it to process images and it seems SIGNIFICANTLY faster when you are in Loupe mode and it indexing as it is not trying to constantly update stacks and order them as they are added.
Lightroom 6 within in the last couple of days distributed a minor update via Adobe update but the version still shows as 6.0.1 (vs. 6.0.0) and didn’t mention any performance improvements. So no help there.
Post Indexing Experiences
- Post indexing after seeding and initial frustrations of my catalog of 228k RAW images I had about 137,000 faces to identify with the largest stack being about 80 deep and there was less than one screen of image stacks over 50.. so this is going to take me quite a while still to work that number down especially with the performance issues still occurring in the application.
- In particularly facial recognition grid view is still ridiculously slow, simply selecting a row of images can take 5 minutes while you wait for Lightroom.. no CPU or memory spikes while it does it either.
- When you go into people view, it still attempts to do “Finding Similar Faces”, even though indexing is completed and if you pause Facial Recognition. This causes significant pauses and delays in processing if you try to do anything before it is done with this “Finding Similar Faces” process…wait for it… it also creates a TON of noise generating 40,568 “alternative” suggestions not listed in the main view.
So my TIPS for proceeding..
- I find the best way to tackle the now indexed photos is going back and doing directories of about a 1000 images or less, and even then sometimes has a bit of lag but mostly is barely notable, if I get over 2000 images I start to notice it even with large stacks.
- I then , if it is all of a single or small number individual (such as from a portrait shoot) I select all images in the directory and then go and deselect any images not them or false positive facial recognitions (such as bushes) and ignore ALL suggestions if I know who they are. I then type their name and let auto-complete address the name if it is an indexed person and I basically override all suggestions. (So basically for the most part completely devaluing facial recognition and recopying the keywords I already had in place).
- I then go to small groups and repeat the process for them just with more selecting bulk selects.
- I then proceed to filter out the biggest bang for the buck crowd directories where I do not care. This includes people’s faces in airshows, street photography, weddings (eliminating all but the wedding party), concerts, etc. Going in and getting rid of facial recognition on these directories in bulk. This will HUGELY reduce your number of phases to identify.. what is not clear yet is if you can later go back and “re-process” the directory for facial recognition without having to also manually identify each one.
- I then go to larger top level directories of about 1000-2000 images.
- I take on everything else.
- MOST IMPORTANTLY, every so often go back and check your named people to make sure the faces at least match and you do not have some bad seedings that delays in Lightroom didn’t accidentally result in people getting labeled incorrectly. Do this by clicking on the named people and then when you find someone who is not that person click on them and give them the proper name just like in the main view.
At the current rate I am estimating I will be retagging images for months, maybe they will have an update by the time I get done but not seeing how it will work into my regular workflow and maybe just be a background idle process activity.
- My catalog went from about 3.3 gigs optimized to just over 4 gigs.. so about about a 20% increase in size by cataloging the faces within my catalog. (Note: I didn’t look at my catalog size before hand, but compared current vs. last backup so this number is not precise, just an estimate)
- There have been many posts about people grumbling about it identifying faces in magazines or billboards in the background.. I honestly like that it can do that as it helps with identifying people in a crowd.. though it needs to be better. I find it only gets about 70%-90% of the people in a wedding formal photo’s face and misses the others, even the ones completely standing out (like the bride and groom).
- I have noticed it tends to have high false positives on some women’s floral dresses, especially if there is a grid pattern and the flowers are within the grid pattern.
- I REALLY wish I could disable facial recognition on a particular directory. A have several of directories of event and street photography where there are large crowds of people and it picks up dozens of faces to identify of random people I will never know (or probably care) what their name was. Yes I can reject selection and then delete facial recognition markers for those images, but that creates a lot more work for me.
- I have noticed it commonly identifies double round edge numbers, especially “22”, as a face. I have seen at least 2 unique cases of this where 22 was on bleachers in one case and 22 was randomly in the background on something else. After I saw it a couple times I took a screenshot for an example, in this particular example it came up with at least 4 different names for the front of a treadmill in a gym from an fitness shoot. I did go back just to be sure and checked the 4 names lists and NONE of them also had been accidentally trained to recognize “22” as that person too.