Facial Recognition Experiments on a Robotic System Using One-Shot Learning

Abstract

One of the crucial tasks during the interaction human-robot is the face recognition task on digital images. Firstly, the task of recognition requires face detection and a continuing procedure for extracting face characteristics and dealing with the brightness of the environment's faces positioned at different angles and sometimes occlusion problems. In this paper, the aim is to explore the One-shot learning technique, which considers only one image of each person for face detection and uses information extracted from other image databases for this. One of its modifications, Face Recognition algorithm, is applied to recognize people during sessions of interaction with a humanoid robot. This algorithm uses the Dlib capabilities to detect, extract and recognize faces through bases with a singular face image. The results of the experiments performed are presented and show how useful is this kind of learning technique for interactions of a robot with humans.