kandi X-RAY | OpenCV-Face-Recognition Summary
kandi X-RAY | OpenCV-Face-Recognition Summary
Real-time face recognition project with OpenCV and Python
Top functions reviewed by kandi - BETA
- Get faces and labels for each face .
OpenCV-Face-Recognition Key Features
OpenCV-Face-Recognition Examples and Code Snippets
Trending Discussions on OpenCV-Face-Recognition
I have a project where I need to include
face recognition in it. I am referring to this article. This article is using
open-face to get the face
embeddings and its saving all the embeddings in a pickle file. Then its passing the face embeddings data to
support vector machine which generates another pickle file. This file is later used to recognize and predict the face.
This has been working and is giving me more than 80% accuracy. But this article has not explained on how to calculate
euclidean distance. This I needed for my own research work.
I can easily calculate
euclidean distance between the face embedding of test image and face embeddings present in pickle file but I am not able to understand how to set the threshold value so that any distance more than that will be tagged as
Can anyone please point me to some article where this has been explained and I can follow up from there. I have tried searching many articles but didnt get much results on this. Please help. Thanks...
ANSWERAnswered 2020-Apr-15 at 15:45
from sklearn.preprocessing import LabelEncoder from sklearn.svm import SVC import argparse import pickle # construct the argument parser and parse the arguments ap = argparse.ArgumentParser() ap.add_argument("-e", "--embeddings",default=r"C:\Users\osama\Desktop\opencv-face-recognition\face_detection_model\output", help="path to serialized db of facial embeddings") ap.add_argument("-r", "--recognizer", default=r"C:\Users\osama\Desktop\opencv-face-recognition\face_detection_model\output", help="path to output model trained to recognize faces") ap.add_argument("-l", "--le", default=r"C:\Users\osama\Desktop\opencv-face-recognition\face_detection_model\output", help="path to output label encoder") args = vars(ap.parse_args()) # load the face embeddings print("[INFO] loading face embeddings...") data = pickle.loads(open(args["embeddings"], "rb").read()) # encode the labels print("[INFO] encoding labels...") le = LabelEncoder() labels = le.fit_transform(data["names"]) # train the model used to accept the 128-d embeddings of the face and # then produce the actual face recognition print("[INFO] training model...") recognizer = SVC(C=1.0, kernel="linear", probability=True) recognizer.fit(data["embeddings"], labels) # write the actual face recognition model to disk f = open(args["recognizer"], "wb") f.write(pickle.dumps(recognizer)) f.close() #write the label encoder to disk f = open(args["le"], "wb") f.write(pickle.dumps(le)) f.close()
ANSWERAnswered 2020-Jan-04 at 17:47
You are unpickling an object here:
I went through Pyimagesearch face Recognition tutorial, but my application need to compare two faces only, I have embedding of two faces, how to compare them using opencv ? about the trained model which is use to extract embedding from face is mentioned in link, I want to know that what methods I should try to compare two face embedding.
(Note: I am new to this field)...
ANSWERAnswered 2019-Nov-14 at 05:16
First of all your case is similar to given tutorial, instead of multiple images you have single image that you need to compare with test image,
So you don't really need training step here.
You can do
I want to create a directory of the input taken from my python program and give that as an argument to an argParser which is in the same code.
I just basically want to make a directory of the file name the user enter and run so that i can make a dataset of his images into that specified folder....
ANSWERAnswered 2019-Aug-29 at 17:54
You should make
--output argument as "not required", and set
dirpath as default value:
First of all here is my github link for the question.
And here is my question:
I would like to do a face comparison function using Python. And I can successfully(?) recognize faces using OpenCV. Now, how do I do the comparison thing?
What I understand is this:
In general Machine learning approach, I need to gather lots of data about that particular person and finalize it using a CNN.
However, I just got 2 images, how do I do the comparison? Should I think it in terms of classification or clustering (Using KNN)?
Thank you very much in advance for all your help....
ANSWERAnswered 2017-Sep-12 at 10:02
You need to learn similarity metric for faces. It will allow to extract features good to distinguish different persons. Then you'll be able to find dissimilarity (distance) between them. You can read in more detail here for instance. kNN and such things are useful to find groups of similar faces, but it need to use features, extracted before.
No vulnerabilities reported
You can use OpenCV-Face-Recognition like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
Reuse Trending Solutions
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page