Own Expression dataset(Note: You can downlaod expression images from google, or you can record your video make diffrent expression ,and
converts into Grayscale images(For more accurate prediction))
What steps you have to follow??
Download my repository
Make Images folder in your project ,make subfolder for emotions like Happy,sad,Angry.
Put Face_crop.py & haarcascade_frontalface_alt.xml in every type of image folder,ex : put this program in "happy' image folder and
run this program it will detect faces from images and convert it into grayscale and make a new images in same folder.
After that you have to create model, for that copy code from code.txt file and open CMD in your project folder and paste it & enter
It will take training aaround 20-25 minutes so keep patience.
After training it will create two files retrained_graph.pb & retrained_labels.txt
Now run recognition_webcam.py.
If you want to fetch video from your mobile cam than use android_recognition.py,but you have to install IPWebcam app in your system
and replace your server URL with my URL
That's all
How it works? See:)
Notes
It will require high processing power(I have 8 GB RAM & 2 GB GC)
If you think it will recognise expression just like humans,than leave it ,its not possible.
Download 300 Images for every expression(you can use batch downloader)
Noisy image can reduce your accuracy so quality of images matter.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent