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Ellsionjeep / Emotion-recognition

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README
MIT

Project Name : Emotion-recognition

Table of Content :

1.Description

2.Installations

3.Usage

4.Dataset

Description:

Our Human face is having a mixed emotions so we are to demonstrate the probabilities of these emotions that we have.

What does Emotion Recognition mean?

Emotion recognition is a technique used in software that allows a program to "read" the emotions on a human face using advanced image processing. Companies have been experimenting with combining sophisticated algorithms with image processing techniques that have emerged in the past ten years to understand more about what an image or a video of a person's face tells us about how he/she is feeling and not just that but also showing the probabilities of mixed emotions a face could has.

Installations:

-keras

-imutils

-cv2

-numpy

Usage:

The program will creat a window to display the scene capture by webcamera and a window representing the probabilities of detected emotions.

Demo

python real_time_video.py

You can just use this with the provided pretrained model i have included in the path written in the code file, i have choosen this specificaly since it scores the best accuracy, feel free to choose any but in this case you have to run the later file train_emotion_classifier

If you just want to run this demo instead of training the model from scaratch, the following content can be skipped

Train

python train_emotion_classifier.py

Dataset:

I have used this dataset

Download it and put the csv in fer2013/fer2013/

-fer2013 emotion classification test accuracy: 66%

Credits

This work is inspired from this great work and the resources of Adrian Rosebrock helped me alot!.

Ongoing

Draw emotions faces next to the detected face.

Issues & Suggestions

If any issues and suggestions to me, you can create an issue.

If you like this work please help me by giving me some stars.

MIT License Copyright (c) [2018] [Omar Ayman] Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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