davisking/dlib-models

mmod_human_face_detector.dat and WIDER dataset

kaisark opened this issue · 1 comments

kaisark commented

Hi. I tested DLIB facial detection using CNN (zero upscale) and mmod_human_face_detector.dat against WIDER style dataset type video using a selfie mask filter (tiktokapp) and the results look pretty good.

I think the WIDER dataset definitely represents some of the new frontiers/challenges of facial detection/recognition. Do you think its possible to perform facial recognition/identification given occlusion like in the WIDER dataset (http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/) using simple RGB monocam imagery or do you think that different sensors will be needed?

mmod_human_face_detector.dat:
"This is trained on this dataset: http://dlib.net/files/data/dlib_face_detection_dataset-2016-09-30.tar.gz.
I created the dataset by finding face images in many publicly available image datasets (excluding the FDDB dataset). In particular, there are images from ImageNet, AFLW, Pascal VOC, the VGG dataset, WIDER, and face scrub." - https://github.com/davisking/dlib-models

facedetect-dlib-computervision gif-downsized_large

davisking commented

Not really, since I wouldn't be able to recognize people based on many of the images in WIDER. A good rule in computer vision is if a human can't do it (with RGB images) then a computer isn't going to be able to do it either.

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