Face-Recognition | Face Recognition using pre-trained model | Computer Vision library
kandi X-RAY | Face-Recognition Summary
kandi X-RAY | Face-Recognition Summary
This repo contains face_verify.py and app.py which is able to perform the following task -.
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
- 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
Face-Recognition Key Features
Face-Recognition Examples and Code Snippets
Community Discussions
Trending Discussions on Face-Recognition
QUESTION
I have probem with this code , why ?
the code :
...ANSWER
Answered 2021-Apr-09 at 09:33Use
from tensorflow.keras.
instead of from keras.
QUESTION
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 07:45This 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:
QUESTION
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 05:20Sounds like you want a nearest neighbour search. You could have a look at the various space partitioning data structures like kd-trees
QUESTION
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 12:11You need to change the order of function declaration. You can not call const
variables before they were declared.
QUESTION
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 08:46Sorry 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.
QUESTION
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 16:26It 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:
QUESTION
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 10:50Your response is JSON, you just need to use json_decode()
function like
QUESTION
I am getting when face_encodings function is being called .
...ANSWER
Answered 2021-Feb-06 at 13:00The issue was the parameter variable which i passed to face_encoding()
correct :
QUESTION
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 13:35When 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:
QUESTION
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 06:00if 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
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install Face-Recognition
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
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page