Automatic selection of user samples for a non-collaborative face verification system

Abstract

This paper describes the challenges that involve developing a software capable of capturing users’ faces on mobile devices in a non-collaborative environment. The goal is to generate a set of quality training samples of the user’s face for the construction of a model that can be used in a later phase of biometric identification. To this end, a supervised learning system is integrated to determine when a photo should be taken. This learning is supported by a varied input data set that contains information regarding the pose of the device, its manipulation and other environmental factors such as lighting. The software also has different ways of working with the objective of not wasting resources and be little invasive. Working modes are managed with an easy-to-maintain and scalable rules-based system. The experimental results show the robustness of the proposal.