facial-expression-recognition-svm | Training SVM classifier to recognize people | Machine Learning library

by amineHorseman Python Version: Current License: GPL-3.0

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | facial-expression-recognition-svm Summary

facial-expression-recognition-svm is a Python library typically used in Artificial Intelligence, Machine Learning applications. facial-expression-recognition-svm has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. However facial-expression-recognition-svm build file is not available. You can download it from GitHub.

Training SVM classifier to recognize people expressions (emotions) on Fer2013 dataset

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Quality

Security

License

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Support

facial-expression-recognition-svm has a low active ecosystem.

It has 113 star(s) with 56 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 5 open issues and 5 have been closed. On average issues are closed in 57 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of facial-expression-recognition-svm is current.

Quality

facial-expression-recognition-svm has 0 bugs and 0 code smells.

Security

facial-expression-recognition-svm has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

facial-expression-recognition-svm code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

facial-expression-recognition-svm is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

facial-expression-recognition-svm releases are not available. You will need to build from source code and install.

facial-expression-recognition-svm 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.

facial-expression-recognition-svm saves you 142 person hours of effort in developing the same functionality from scratch.

It has 356 lines of code, 7 functions and 5 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Train the model
Load data
Compute the accuracy of the given model
Train function to minimize hyperparameters
Train the SVC model
Get new label
Generate a list of hog windows
Given an image and a set of rects return a bounding box

Get all kandi verified functions for this library.

facial-expression-recognition-svm Key Features

No Key Features are available at this moment for facial-expression-recognition-svm.

facial-expression-recognition-svm Examples and Code Snippets

No Code Snippets are available at this moment for facial-expression-recognition-svm.

Community Discussions

Trending Discussions on facial-expression-recognition-svm

X.shape[1] size doesn't fit the expected value

QUESTION

X.shape[1] size doesn't fit the expected value

Asked 2020-Apr-06 at 22:04

I'm currently working on my final degree project in robotics, and I decided to create an open-source robot capable of replicating human emotions. The robot is all set up and ready to receive orders, but I'm still busy coding it. I'm currently basing my code off this method. The idea is to extract 68 facial landmarks from a low FPS video feed (using RPi Camera V2), feed those landmarks to a trained SVM classifier and have it return a numeral from 0-6 depending on the expression it detected (Angry, Disgust, Fear, Happy, Sad, Surprise and Neutral). I'm testing out the capabilities of my model with some pictures I took using the RPi Camera, and this is what I've managed to put together so far in terms of code:

...

ANSWER

Answered 2020-Apr-06 at 22:04

Solved it! Turns out my model was trained using a combination of HOG features and Dlib landmarks, however I was only feeding the landmarks to the predictor, which resulted in the size discrepancy.

Source https://stackoverflow.com/questions/61015323

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

Vulnerabilities

No vulnerabilities reported

Install facial-expression-recognition-svm

You can download it from GitHub.
You can use facial-expression-recognition-svm 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.