deepface VS face-parsing.PyTorch

You might also want to categorize using either mediapose's holistic model which includes the face mesh or maybe something that gauges emotion like this: https://github.com/serengil/deepface

This is another one I tried but requires programming knowledge. https://github.com/serengil/deepface

It must be a matter of time before we have this, we have so many library's that can detect a face expression. For example: https://github.com/serengil/deepface

I bought a On1 license, but as you point out, it has no facial recognition nor any plans to add it. That also means it has no concept of face regions as implemented in XMP, so that cannot be retrofitted by running face recognition externally, e.g. using an open-source solution like Deepface.

deepface - newer version which has multiple face detectors in it you can mess with, dlib included. Also does face recognition.

If you're looking for models / planning to write some code yourself: Have a look at ArcFace for recognition and RetinaFace for face detection by Insightface. DeepFace repository might also be worth a try.

DeepFace: https://github.com/serengil/deepface

DeepFace utilizes a collection of different face detection models listed here: models = ["VGG-Face", "Facenet", "Facenet512", "OpenFace", "DeepFace", "DeepID", "ArcFace", "Dlib", "SFace"]

Segmentation of head / body - I'd either use mediapipe pose and make something cleverly cut where I wanted based on the landmarks, or I'd use pytorch face parsing if you want to be very exact. I found both of these fairly easy to get to run.

BiSeNet

Uses a mix of instance segmentation (BiSeNet) and conditional GAN, and is heavily inspired by the Pix2PixHD and DeepSIM papers. Will have more details when I wake up!

I've developed a demo of human face make-up. The main tech used is face parsing.To get lips, eyes and eyeglasses mask, I perform face parsing based on face-parsing.PyTorch. After that, color changing is done in HSV color space.