A real-time facial emotion recognition model using CNNs. For the facial emotion recognition part, we have used Convolutional Neural Networks which take a real-time image as an input and classify it as one of the 5 emotions - fear, angry, happy, sad, neutral. For this we have also used libraries like NumPy, Pandas, Scikit Plot, Matplotlib, Keras, OpenCV and TensorFlow/PyTorch. For the pre-processing of the input images, we have used OpenCV for face detection and grayscale conversion in order to make it suitable for the model.
The webapp also keeps a track of the emotions of all the people accessing the website.
The project aims to create a Machine-Learning based model that will be able to suggest possible methods/modes/routes to help suggest a public transport infrastructure for an under-developed or proposed city in the most efficient way possible using libraries, conventional and non-conventional algorithms.