Portrait photo segmentation and generation with Deep Neural Network

In this project, the main purpose is to develop a tool for facial image anonymization by replacing the face in an image with a fake, generated face. To have realistic looking generated images, it is essential for the generated face to have a similar pose to the original one and to be seamlessly harmonic to the background. To achieve this goal, the first step is to locate all the faces of humans in images, and then extract key information about the pose (e.i.: eyebrows alignment) to reconstruct a similar face. Finally, a deep network will be trained to draw a fake face with extracted facial information on the location where faces are detected. This can be served as a data anonymization application protecting user’s privacy. In addition, it can be a tool for data augmentation which is a commonly used technique to boost deep neural networks’ performance.

Faculty Supervisor:

Ioannis Mitliagkas

Student:

Partner:

Jumio

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Université de Montréal

Program: