Face distance estimation from a monocular camera

A new era of mobile devices (phones and tablets) has emerged with some of them possessing a front camera facing the person or user and stereo cameras in the rear side. This paper emphasise on a novel method to use such devices with monocular camera to determine the depth between the user and the front camera using a back propagation neural network (BPNN). This depth is successively used to calculate the zooming factor for a legible view, to read a document on the display of the mobile device. In the absence of camera parameters and also considering the fact that the camera and user's face are in constant motion; measuring the distance of the face from the camera becomes a hard problem. We propose the use of frontal facial features acquired from the monocular camera to find the depth information with the use of supervised learning algorithm. Training the BPNN for the facial features for a standard face is one time factory programmed during camera manufacturing. One time new user's face registration for the gadget using rear cameras followed by the simulation of depth from the front camera using the trained BPNN is proposed in this work. The results of distance estimation are likened with stereo camera setup and the approach is validated.