Feeding a DNN for face verification in video data acquired by a visually impaired user

Some experiments on a face verification tool based on FaceNet are presented in this paper. The task of the system is to perform face verification in a real-time assistive system aiming at facilitating the approach between a blind person and a preselected acquaintance of his/her who enters the field of view. Face detection is made easier by the fact that an almost frontal view of the face is highly probable; verification on the contrary is difficult due to the poor quality of the acquired images and to the necessity of achieving a very low error rate. A custom database consisting of subjects required for verification is populated with face images provided by a suitable detection tool. The cascade of FaceNet and a Bayesian Classifier proves to be an effective tool for this unconstrained face verification task.