emotion-detection-using-TF

This repository contains a Tensorflow model trained on FER-2013 dataset for emotion detection.

This dataset consists of 35887 grayscale, 48x48 sized face images with seven emotions - angry, disgusted, fearful, happy, neutral, sad and surprised.

Dependencies

• Python 3, Matplotlib, OpenCV, Tensorflow
• To install the required packages, run
  $pip install -r requirements.txt.

How to use this repo

• First, clone the repository and enter the folder

git clone https://github.com/Dipeshtamboli/emotion-detection-using-TF.git
cd emotion-detection-using-TF

• Then use this saved weights model.h5 directly for the inference purpose.

python inference.py --filename img.jpg

Here, we need to give the argument for test image name.

Algorithm

• First, the haar cascade method is used to detect faces in each frame of the webcam feed.

• The region of image containing the face is resized to 48x48 and is passed as input to the CNN.

• The network outputs a list of softmax scores for the seven classes of emotions.

• The emotion with maximum score is displayed on the screen.

Example Output

Here, the sample input image is of the size 4.16 MB and it is till able to detect it.