Are there any libraries/packages in Python which help detect landmarks from facial images and then compute facial measurements like the distance between the eyes, eyebrow length, lip thickness...?
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I'm using dlib and mediapipe to get facial landmarks arround a face image. My question is about the jittering. In Mediapipe, the researchers advise us to use temporal filter like the one euro filter to reduce the jittering. But I noticed that, in two consecutives frames that are similar, without mouvement, the facial landmarks is not stable. So I observe the detected bounding box arround the face, and it is not stable too. Is it a common solution to stabilized the detected bounding box before the estimation of the facial landmarks?
I have face recognition model in Python which detects face and differentiates the features like eyes, nose, mouth from a face. I want to filter-out the face features (face parts) depending upon their sizes, so that I can easily get persons if they have big eyes or small eyes without displaying the whole data.
Can anyone suggest what should I use to detect facial feature's size?
We are newbie in the field of programming and we want to learn more, or you could help us code, learn more about YOLO (real-time object detection) with distance measurement from the camera to the object. Also we want the output to be in the form of audio. For example, a car has been detected and there will be an audio recording saying "A car has been detected at a distance of ...cm.
There are two different approaches. One would be to train the YOLO network to output the distance to the detected object along with the outher outputs. This might be well hard and time consuming, especially if you are new to this DNN stuff. Another, easier, way would be to get the bounding box size from the YOLO detections, and calculate the distance based on the known car size against the bounding box size (the smaller is the bounding box -- the farther is the car).
Don't expect cm precision with all this, you'd be lucky if you get your precision within 10%, that's 10m for 100m distance.
How can I use Python and OpenCV to find facial similarity?
I've successfully used OpenCV and Python to extract faces from multiple photographs using Haar Cascades.
I now have a directory of images, all of which are faces of different people.
What I'd like to do is take a sample image, and then see which face it most looks like.
I've tried using pyssim:
pyssim needle.jpg "haystack/*"
But, unfortunately, it's looking a image similarity (colours etc) rather than facial features.
To reiterate - I'm quite happy detecting faces, what I'd like to be able to do is compare them.
I have a not-so-simple question.
The Situation:
I'm working on robust facial detection API in python written on top of OpenCV (cv not cv2).
I am using Haar Cascades for face detection specially
front - haarcascade_frontalface_default.xml
profile - haarcascade_profileface.xml
Each worker is using different harr classifier (front/profile) and produce the set of ROI (Region of Interests) then do a uion on them and merge all overlaping bouding boxes.
The result is "your casual red square" around a face with 70% accuracy and not so may phantom faces.
The problem:
Simply tilting the face. My algorithm cannot detect a tilted face.
For profile detection I did a simple flip of a image to detect both left and right profile.
I was thinking there "should" be a better way to detect a tilted face than to call algorithm multiple times for multiple slightly rotated images. (This is a only solution that came to my mind).
The question:
Is there a approach or a way or a specific harr classifier for detection of tilted faces?
Thank you :)