OpenCV-Face-Recognition | Real-time face recognition project with OpenCV and Python | Computer Vision library

 by   Mjrovai Python Version: Current License: No License

kandi X-RAY | OpenCV-Face-Recognition Summary

kandi X-RAY | OpenCV-Face-Recognition Summary

OpenCV-Face-Recognition is a Python library typically used in Artificial Intelligence, Computer Vision, OpenCV applications. OpenCV-Face-Recognition has no bugs, it has no vulnerabilities and it has medium support. However OpenCV-Face-Recognition build file is not available. You can download it from GitHub.

Real-time face recognition project with OpenCV and Python

            kandi-support Support

              OpenCV-Face-Recognition has a medium active ecosystem.
              It has 970 star(s) with 965 fork(s). There are 55 watchers for this library.
              It had no major release in the last 6 months.
              There are 26 open issues and 2 have been closed. On average issues are closed in 466 days. There are 9 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of OpenCV-Face-Recognition is current.

            kandi-Quality Quality

              OpenCV-Face-Recognition has 0 bugs and 7 code smells.

            kandi-Security Security

              OpenCV-Face-Recognition has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              OpenCV-Face-Recognition code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              OpenCV-Face-Recognition does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              OpenCV-Face-Recognition releases are not available. You will need to build from source code and install.
              OpenCV-Face-Recognition has no build file. You will be need to create the build yourself to build the component from source.
              OpenCV-Face-Recognition saves you 77123 person hours of effort in developing the same functionality from scratch.
              It has 85632 lines of code, 1 functions and 11 files.
              It has low code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed OpenCV-Face-Recognition and discovered the below as its top functions. This is intended to give you an instant insight into OpenCV-Face-Recognition implemented functionality, and help decide if they suit your requirements.
            • Get faces and labels for each face .
            Get all kandi verified functions for this library.

            OpenCV-Face-Recognition Key Features

            No Key Features are available at this moment for OpenCV-Face-Recognition.

            OpenCV-Face-Recognition Examples and Code Snippets

            No Code Snippets are available at this moment for OpenCV-Face-Recognition.

            Community Discussions


            How to do face recognition using euclidean distance in python
            Asked 2020-Apr-15 at 15:45

            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 unknown.

            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



            Answered 2020-Apr-15 at 15:45

            You can build 2 ( normal ) distributions.

            1. distances between same person's faces
            2. distances between different faces

            Intersection of these distributuins will be the threshold.

            Small illustration:



            LabelEncoder object is not subscriptable
            Asked 2020-Jan-04 at 17:47
            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)
      ["embeddings"], labels)
            # write the actual face recognition model to disk
            f = open(args["recognizer"], "wb")
            #write the label encoder to disk
            f = open(args["le"], "wb")


            Answered 2020-Jan-04 at 17:47

            You are unpickling an object here:



            Open cv compare two face embeddings
            Asked 2019-Nov-14 at 11:42

            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)



            Answered 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



            How can I remove giving args in the terminal and add my own args inside the argparse
            Asked 2019-Aug-30 at 03:00

            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.



            Answered 2019-Aug-29 at 17:54

            You should make --output argument as "not required", and set dirpath as default value:



            Face comparison (Not recognition or detection) using OpenCV and Keras?
            Asked 2018-Sep-27 at 10:02

            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.



            Answered 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.


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install OpenCV-Face-Recognition

            You can download it from GitHub.
            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.


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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