EigenFace | EigenFace implementation in Python for Yale FaceDatabase | Computer Vision library

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kandi X-RAY | EigenFace Summary

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

EigenFace implementation in Python for Yale FaceDatabase.

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EigenFace has a low active ecosystem.

It has 18 star(s) with 3 fork(s). There are 6 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 1 have been closed. On average issues are closed in 3 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of EigenFace is current.

Quality

EigenFace has 0 bugs and 0 code smells.

Security

EigenFace has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

EigenFace code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

EigenFace 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.

Reuse

EigenFace releases are not available. You will need to build from source code and install.

EigenFace has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed EigenFace and discovered the below as its top functions. This is intended to give you an instant insight into EigenFace implemented functionality, and help decide if they suit your requirements.

Plot the eigenvectors
Get image shape
Returns the number of components to preserve variance
Get eigenvalue distribution
Plots the eigenvector
Plot the average weight matrix
Returns the weighted average weight matrix
Plot the eigenvalue distribution

Get all kandi verified functions for this library.

EigenFace Key Features

No Key Features are available at this moment for EigenFace.

EigenFace Examples and Code Snippets

No Code Snippets are available at this moment for EigenFace.

Community Discussions

Trending Discussions on EigenFace

How to use Linear Discriminant Analysis to identify an image in the training folder with a similar image in the testing folder?

Uploading multiple images of faces (5-10 face images, same dimensions), change to matrix and perform PCA from scratch in R shiny

How to get best eigenface using opencv python

QUESTION

How to use Linear Discriminant Analysis to identify an image in the training folder with a similar image in the testing folder?

Asked 2021-Nov-23 at 21:32

I have a windows forms project and I have a dataset called the ATTDataSet, the folder has two folders, the training and testing folders, each contain similar images in the same order. I have a form with two picture box with the labels original image and recognized image. I need help with processing an image from the training folder to train this program to save the name of that image and identify a similar image in the testing folder with this algorithm Linear Discriminant Analysis. I want to train the program first to learn a face and then later I will implement a button handler to identify all the images in the second folder with the same pattern as that which was recognized in the learn process. The code am using is below

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ANSWER

Answered 2021-Nov-23 at 21:32

You would need at least some linear algebra library which can compute vector-matrix multiplication and an inverse of a matrix. In such case you compute covariance of all your data, take it's inverse, and the discriminative vector is then a vector between means (centers) of two classes multiplied by the inverse.

There are libraries which implement LDA. For example Egmu CV (which is sort of OpenCV in C#) has FisherFaceRecognizer which should implement Fischer Discriminant Analysis which is almost exactly LDA (the differences are not important). https://www.emgu.com/wiki/files/4.5.1/document/html/80c70818-c4e1-e5a7-6c74-1ce3d6bd1be4.htm

Anyway, LDA for face recognition is not a good idea and it will not work much (almost at all). Reasonable approach is to take a CNN specifically trained to extract facial embeddings and compare those embedding using simple distance function (e.g. L2) to get a facial identity distance of images. Many "deep learning" frameworks can be used from C# and you can download pretrained face recognition networks.

Source https://stackoverflow.com/questions/69976173

QUESTION

Uploading multiple images of faces (5-10 face images, same dimensions), change to matrix and perform PCA from scratch in R shiny

Asked 2020-Nov-04 at 23:52

kindly help. I'm new to programming and i'm a bit confused.

I need to take an upload of 5 -10 pictures and perform PCA from scratch on them. but my plot output for the second tab is not rendering for the prop table . ** ALL Images display well on the first tab**, the issue comes on the processes after. ill appreciate some clarity on how to use Rshiny, i feel like I'm missing something that's fundamental.

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ANSWER

Answered 2020-Oct-26 at 01:11

Using the as.list function i was able to bring the images in and get the plots

Source https://stackoverflow.com/questions/64472164

QUESTION

How to get best eigenface using opencv python

Asked 2020-Feb-28 at 11:09

I am using this link : https://www.learnopencv.com/eigenface-using-opencv-c-python/ to get the average face of the images and eigenfaces.

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ANSWER

Answered 2020-Feb-28 at 11:09

Eigen faces are sorted in eigenVector from top to bottom. ( by eigen values, from largest to smallest ). So just out first N eigen faces. Usually to show we need to add mean face to eigen faces and show resulting images.

Really eigen faces are ND axes in face image space, mean face is origin, eigen values are proportional to dispersion of input faces set along certain axis (eigen face). First ("best" in your case) is the axis with maximum variance.

So if thinkking in face space, the face is a point in eigen space and you can sinthesize any face as usual point

face(C1,C2,..,CN)=mean_face+ C1*eigen_face1+C2*eigen_face2+...CN*eigen_faceN

You can aso project any face to eigen space and get it's C1,C2,...,CN coordinates.

Using these coordinates you can find "distance" between faces and it usually used to compare faces.

Source https://stackoverflow.com/questions/60448383

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

Vulnerabilities

No vulnerabilities reported

Install EigenFace

You can download it from GitHub.
You can use EigenFace 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.

Support

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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