Neural network identification of people hidden from view with a single-pixel, single-photon detector

TL;DR

This paper demonstrates that a neural network combined with a single-photon, single-pixel detector can identify and locate a hidden person from a small database, simplifying hardware and processing compared to traditional methods.

Contribution

It introduces a novel approach using neural networks with non-scanning, single-pixel detectors for both locating and identifying hidden individuals.

Findings

Successfully identified hidden persons from a small database.

Achieved localization and identification with simplified hardware.

Reduced computational processing compared to scanning systems.

Abstract

Light scattered from multiple surfaces can be used to retrieve information of hidden environments. However, full three-dimensional retrieval of an object hidden from view by a wall has only been achieved with scanning systems and requires intensive computational processing of the retrieved data. Here we use a non-scanning, single-photon single-pixel detector in combination with an artificial neural network: this allows us to locate the position and to also simultaneously provide the actual identity of a hidden person, chosen from a database of people (N=3). Artificial neural networks applied to specific computational imaging problems can therefore enable novel imaging capabilities with hugely simplified hardware and processing times