Filtering Adience prior to benchmarking? about agegenderdeeplearning HOT 3 CLOSED

Hi @avasbr ,

Thank you for your interest in our work.

For that image, it actually looks like 3 different faces in the same image -- the bounding boxes are different. Are you sure this is not the case?

Best,
Gil

from agegenderdeeplearning.

Hi @GilLevi,

You're absolutely right, not sure why I didn't think of that. thanks for clearing that up :) I guess the reason I thought it might be "bad" is because I didn't see any references to the other faces in the train_val_txt_files_per_fold files. It appears that the two images with age labels "3" and "35" look correct, but are eliminated during processing, via this if-clause:

From what I can see, in fold 0 alone there are about ~1.2K images that have labels (that are not "None") that don't correspond to the adience group labels, but aren't necessarily incorrect. I'm wondering if it makes sense to ignore them or to transform them into one of the corresponding group labels? If transforming them is the right thing to do, when you have a label like 35 or 3, which lies precisely between two group labels, i.e (25-32, 38-43) and (0-2, 4-5) respectively, which group should it belong to?

To reiterate, the main reason why I'm asking is to make sure I align with convention when benchmarking, and if convention is to ignore, then that's easy enough to do.

I apologize in advance if this has been discussed elsewhere. Thanks again!

from agegenderdeeplearning.

Hi @avasbr ,

I apologize for the late response. The convention when benchmarking is to ignore those labels (that don't fit into the age groups).

Best,
Gil

from agegenderdeeplearning.