Neural network identification of people hidden from view with a single-pixel, single-photon detector
This work enables non-invasive imaging of hidden objects, particularly for security or surveillance applications, but is incremental due to the small database and specific setup.
The researchers tackled the problem of identifying and locating hidden people using scattered light, achieving the ability to determine both position and identity from a database of three individuals with simplified hardware and faster processing.
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