facial-expression-recognition | Facial expression recognition deep learning | Machine Learning library

by lazyprogrammer Python Version: Current License: No License

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

kandi X-RAY | facial-expression-recognition Summary

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

Facial expression recognition deep learning examples

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Quality

Security

License

Reuse

Support

facial-expression-recognition has a low active ecosystem.

It has 130 star(s) with 142 fork(s). There are 15 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 1027 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

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

Quality

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

Security

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

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

There are 0 security hotspots that need review.

License

facial-expression-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.

Reuse

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

facial-expression-recognition has no build file. You will be need to create the build yourself to build the component from source.

facial-expression-recognition saves you 327 person hours of effort in developing the same functionality from scratch.

It has 784 lines of code, 70 functions and 10 files.

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

Top functions reviewed by kandi - BETA

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

Fit the convolutional model
Perform the forward computation
Predict the maximum value of X
Compute the mean error rate between two targets
Fit the model
Perform forward computation
Softmax op
Relu function
Fit the optimizer
Compute the softmax
Compute the cost of the cost function
Get the training data
Get binary data
Get image data
Compute the error score
Compute the forward matrix

Get all kandi verified functions for this library.

facial-expression-recognition Key Features

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

facial-expression-recognition Examples and Code Snippets

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

Community Discussions

Trending Discussions on facial-expression-recognition

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

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

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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