This project aims to predict different facial expressions given a neutral model, to populate a Neural Network dataset.

For this purpose we made some statistics analysis of a set of facial expressions. After these studies, we achieved to build an efficient facial expression predictor that can translate a neutral model to an expressive face.

The expressions studied are:

• angry;
• contempt;
• disgust;
• fear;
• happy;
• sadness;
• surprise.

The project is developed by Alessandro Sestini and Francesco Lombardi

In order to strengthen the results, different techniques have been used:

• Mean, median, mode
• Linear regression
• Support Vector Regression (SVR)
• Neural Network

After a study of data distributions through Mean Shift algorithm, we can conclude that the best method to create a good quality facial expression is the statistics method.

A result example is shown in the figure below; the script let the user to emphasize the expression changing an alpha value.:

To run the project follow these instructions:

1. download the repository;

2. open main.py and change the values in order to obtain the expressions you prefer. You can follow the instructions on the comments;

3. the script will output both the neutral and expressive faces.

*the installation procedure for Matlab engine for Python is available here

A copy of the report (italian) can be found here.

Licensed under the term of MIT License.