Create your own emoji with Deep Learning Deep Learning project for beginners
Emojis or avatars are ways to indicate nonverbal cues. These cues have become an essential part of online chatting, product review, brand emotion, and many more. It also lead to increasing data science research dedicated to emoji-driven storytelling.
With advancements in computer vision and deep learning, it is now possible to detect human emotions from images. In this deep learning project, we will classify human facial expressions to filter and map corresponding emojis or avatars.
The FER2013 dataset ( facial expression recognition) consists of 48*48 pixel grayscale face images. The images are centered and occupy an equal amount of space. This dataset consist of facial emotions of following categories:
0:angry
1:disgust
2:feat
3:happy
4:sad
5:surprise
6:natural
In the repo steps will build a convolution neural network architecture and train the model on FER2013 dataset for Emotion recognition from images.
Download the dataset. Extract it in the data folder with separate train and test directories.
In this deep learning project for beginners, I have built a convolution neural network to recognize facial emotions. I have trained our model on the FER2013 dataset. Then I mapped those emotions with the corresponding emojis or avatars.
Using OpenCV’s haar cascade xml I got the bounding box of the faces in the webcam. Then I have feed these boxes to the trained model for classification.
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(Note It's existing project with little changes done by Saranraj)