happy

neutral

sad

angry

fear

surprise

Facial Expression Recognition

introduction

This model can detect 7 basic human facial expressions, and it's created using tensor flow Keras library. this module can work on your camera and also recorded videos. outputs displayed on web browser localhost:5000. this model can identify

  • happy

  • sad

  • angry

  • neutral

  • fear

  • surprise

  • disgust with 61.86% accuracy.

convolutional neural network

A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning algorithm which can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image and be able to differentiate one from the other. The pre-processing required in a CNN is much lower as compared to other classification algorithms. While in primitive methods filters are hand-engineered, with enough training, CNNs have the ability to learn these filters/characteristics.

The architecture of a CNN is analogous to that of the connectivity pattern of Neurons in the Human Brain and was inspired by the organization of the Visual Cortex. Individual neurons respond to stimuli only in a restricted region of the visual field known as the Receptive Field. A collection of such fields overlap to cover the entire visual area.

Installation and Testing

To test this model you have to install python 3 and these libraries on your test machine, flask, TensorFlow, Numpy, openCV After complete installations direct CMD to project folder and run main.py

on windows

D:\prject>py -3 main.py Then open browser and type localhost:5000 if need to run this model on-device camera change the path to 0 in VideoCamera class in camera.py


links