Face-Recognition | Face Recognition using pre-trained model | Computer Vision library

 by   paul-pias Python Version: Current License: No License

kandi X-RAY | Face-Recognition Summary

kandi X-RAY | Face-Recognition Summary

Face-Recognition is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Pytorch, Tensorflow, Keras applications. Face-Recognition has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

This repo contains face_verify.py and app.py which is able to perform the following task -.
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              Face-Recognition has a low active ecosystem.
              It has 40 star(s) with 18 fork(s). There are 3 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 5 open issues and 2 have been closed. On average issues are closed in 44 days. There are 1 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of Face-Recognition is current.

            kandi-Quality Quality

              Face-Recognition has no bugs reported.

            kandi-Security Security

              Face-Recognition has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

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

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              Face-Recognition releases are not available. You will need to build from source code and install.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed Face-Recognition and discovered the below as its top functions. This is intended to give you an instant insight into Face-Recognition implemented functionality, and help decide if they suit your requirements.
            • Bulk detect face detection
            • Computes the intersection of the given boxes
            • Compute bounding box
            • Generate a bounding box for a given image
            • Detects the faces of the image
            • Calibrate bounding boxes
            • Generate bounding boxes from bounding boxes
            • Convert boxes to square boxes
            • Read faces and align them
            • Find the similarity between two vectors
            • Forward convolutional layer
            • Store the revision info
            • Forward projection of embeddingings
            • Get a list of blocks
            • Setup the module
            • Returns a dictionary with the configuration
            • Decorator for a layer
            • Get train loader
            • Inverse of transformation
            • Create TMNN layer
            • Creates input pipeline
            • Prepare facebank
            • Detects face of a given image
            • Train the optimizer
            • Calculates the distance between two embeddings
            • Find similarity between two points
            • Finds the optimal learning rate for the given model
            Get all kandi verified functions for this library.

            Face-Recognition Key Features

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

            Face-Recognition Examples and Code Snippets

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

            Community Discussions

            QUESTION

            Error in "from keras.utils import to_categorical"
            Asked 2021-Jun-04 at 00:33

            I have probem with this code , why ?

            the code :

            ...

            ANSWER

            Answered 2021-Apr-09 at 09:33

            Use from tensorflow.keras. instead of from keras.

            Source https://stackoverflow.com/questions/67018079

            QUESTION

            TypeError: __init__(): incompatible constructor arguments with face_recognition call
            Asked 2021-May-22 at 09:33

            I was trying to develop a face recognition attendance system, I coded 100% just like the tutorial, but I still got some errors, here's the code:

            ...

            ANSWER

            Answered 2021-May-22 at 07:45

            This line: for (top, right, bottom, left), name in zip(faceLocations, faceNames): .

            Make sure that top, right, bottom, left values are integer values and not float values. Just print them once to confirm. If they are float values convert them to int using int(). Like this:

            Source https://stackoverflow.com/questions/67647011

            QUESTION

            How to store FaceNet data efficiently?
            Asked 2021-May-15 at 23:18

            I am using the Facenet algorithm for face recognition. I want to create application based on this, but the problem is the Facenet algorithm returns an array of length 128, which is the face embedding per person.

            For person identification, I have to find the Euclidian difference between two persons face embedding, then check that if it is greater than a threshold or not. If it is then the persons are same; if it is less then persons are different.

            Let's say If I have to find person x in the database of 10k persons. I have to calculate the difference with each and every person's embeddings, which is not efficient.

            Is there any way to store this face embedding efficiently and search for the person with better efficiency?

            I guess reading this blog will help the others.

            It's in detail and also covers most aspects of implementation.

            Face recognition on 330 million faces at 400 images per second

            ...

            ANSWER

            Answered 2021-May-11 at 05:20

            Sounds like you want a nearest neighbour search. You could have a look at the various space partitioning data structures like kd-trees

            Source https://stackoverflow.com/questions/67462421

            QUESTION

            react, how to call a function inside then block before initializing it or any proper way?
            Asked 2021-May-13 at 12:11

            I've build an javascript function with face-api.js for my react component which will return/console me the width and height of face detector box. I tried console.log in few places it seems working fine till the models(face-recognition-model).

            But when I write async function for face detector to detect face and console. It gives me error-

            ...

            ANSWER

            Answered 2021-May-13 at 12:11

            You need to change the order of function declaration. You can not call const variables before they were declared.

            Source https://stackoverflow.com/questions/67518994

            QUESTION

            Python face_recognition why not recognise cartoon image?
            Asked 2021-Apr-29 at 08:46

            I am trying to face recognise by python Face-recognition library

            I have tried below code for below image

            Code :

            ...

            ANSWER

            Answered 2021-Apr-29 at 08:46

            Sorry to say but if the face recognition is good it should not recognize cartoon faces, it's designed to recognize human faces and therefore should only tell you how many human faces it is on the image, otherwise it's a bad designed algorithm. If you want a machine-learning algorithm to recognize cartoon faces you would have to train it your self for that specific test.

            I did a quick search on google and the first things I found was an article named "Cartoon Face Recognition: A Benchmark Dataset" at https://arxiv.org/pdf/1907.13394.pdf . Maybe you can find an already existing machine-learning algorithm that have been trained to recognize cartoon faces.

            Hope this helped and I hope you find what you're looking for.

            --------------------------------EDIT--------------------------------

            I found these two git repositories, could be worth looking into more

            https://github.com/srvCodes/Cartoon-Face-Detection-and-Recognition https://github.com/hako/dissertation

            The last link is a link for emotions of cartoon character.

            Source https://stackoverflow.com/questions/67313707

            QUESTION

            FileNotFoundError: [Errno 2] No such file or directory in Opencv face_recognition python
            Asked 2021-Feb-25 at 02:37

            I am trying to make a face_recognition using the face-recognition dlib library, but it gives an error(I am new to python).

            ...

            ANSWER

            Answered 2021-Feb-24 at 16:26

            It says that there is no such directory as a.jpg in the "dataset" folder

            No, that is not what the error means.

            The code is looking for that file in the current directory, not in the dataset folder.

            You did call os.listdir(r'/home/pi/Desktop/dataset/'), but that does not change the current directory.

            Use the full pathname to open the file:

            Source https://stackoverflow.com/questions/66354719

            QUESTION

            How to extract required data from CURL response inside codeigniter?
            Asked 2021-Feb-10 at 10:50

            I tried to use the API from https://rapidapi.com/lambda/api/face-recognition-and-face-detection/details, and got the response as below

            ...

            ANSWER

            Answered 2021-Feb-10 at 10:50

            Your response is JSON, you just need to use json_decode() function like

            Source https://stackoverflow.com/questions/66124131

            QUESTION

            face_encoding function returning an error
            Asked 2021-Feb-06 at 13:00

            I am getting when face_encodings function is being called .

            ...

            ANSWER

            Answered 2021-Feb-06 at 13:00

            The issue was the parameter variable which i passed to face_encoding()

            correct :

            Source https://stackoverflow.com/questions/66077056

            QUESTION

            from numba import cuda, numpy_support and ImportError: cannot import name 'numpy_support' from 'numba'
            Asked 2021-Feb-04 at 13:35

            I am changing pandas into cudf to make faster aggregating and reduce the processing speed. I figure out one library which works on GPU with pandas.

            "CUDF LINK" https://github.com/rapidsai/cudf

            When I entered the below to install in my project it gives an error and I also tried many version of numba.

            ...

            ANSWER

            Answered 2021-Feb-04 at 13:35

            When trying to install cuDF 0.13, conda is apparently finding a numba version that is incompatible with that cuDF 0.13.

            cuDF 0.13 is out of date. The current stable release is 0.17 and the nightly is 0.18. We'll update the README, as it should provide installation instructions for the current version.

            We recommend creating a fresh conda environment. Please try the following conda install command, found here:

            Source https://stackoverflow.com/questions/66046111

            QUESTION

            What does the .api mean in face_recognition.api.face_locations? - face_recognition package python
            Asked 2021-Jan-12 at 06:00

            Is there a difference between face_recognition.api.face_locations and face_recognition.face_locations. The offical documentation explains the use of the former while many example codes use the latter.

            ...

            ANSWER

            Answered 2021-Jan-12 at 06:00

            if you are willing to use your face recognition program from a api provider like Microsoft, im not really sure(even tho i used the library quite a lot). In another way if you are planning to do the coding and processing on your computer, .api funtion is kind of useless(you wont need it).

            Now if you need more inf check out this example that should help you understant the diffrence between the 2 : https://github.com/ageitgey/face_recognition/tree/master/face_recognition

            Hope This helped you :)

            Feel free to ask any question

            Source https://stackoverflow.com/questions/65678425

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

            Vulnerabilities

            No vulnerabilities reported

            Install Face-Recognition

            You can install all the dependencies at once by running the following command from your terminal.
            Although i provided the pretrained model in the work_space/model and work_space/save folder, if you want to download the models you can follow the following url:. I have used the IR-SE50 as the pretrained model to train with my custom dataset. You need to copy the pretrained model and save it under the work_space/save folder as model_final.pth. In the data/facebank you will find a trained model named "facebank.pth" which contains the related weights and "names.npy" contains the corresponding labels of the users that are avialable in the facebank folder. For instance in this case the facebank folder will look like this :-. If you have the "facebank.pth" and "names.npy" files in the data/facebank you can execute the following command to see the demo. and go to the following url from your web browser. First organize your images within the following manner-. now run the following command. You will see a new folder inside the data directory named "processed" which will hold all the images that contains only faces of each user. If more than 1 image appears in any folder for a person, average embedding will be calculated. After executing the script new images for each user in the processed folder will look something like this. Copy all the folders of the users under the data/processed folder and paste in the data/facebank folder. Now to train with your dataset, you need to set args.update == True in line 35 of face_verify.py . After training you will get a new facebank.pth and names.npy in your data/facebank folder which will now only holds the weights and labels of your newly trained dataset. Once the training is done you need to reset args.update==False. However, if this doesn't work change the code in following manner-. Only keep the follwing lines for training, once the training is done just replace it with the old code. Or you can simply pass a command line arguement such as below if there is new data to train. Here the -u parse the command to update the facebank.pth and names.npy. Now you are ready to test the systen with your newly trained users by running-.
            IR-SE50 @ BaiduNetdisk
            IR-SE50 @ Onedrive
            Mobilefacenet @ BaiduNetDisk
            Mobilefacenet @ OneDrive

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