Cyborg groups enhance face recognition in crowded environments

PLoS One. 2019 Mar 6;14(3):e0212935. doi: 10.1371/journal.pone.0212935. eCollection 2019.


Recognizing a person in a crowded environment is a challenging, yet critical, visual-search task for both humans and machine-vision algorithms. This paper explores the possibility of combining a residual neural network (ResNet), brain-computer interfaces (BCIs) and human participants to create "cyborgs" that improve decision making. Human participants and a ResNet undertook the same face-recognition experiment. BCIs were used to decode the decision confidence of humans from their EEG signals. Different types of cyborg groups were created, including either only humans (with or without the BCI) or groups of humans and the ResNet. Cyborg groups decisions were obtained weighing individual decisions by confidence estimates. Results show that groups of cyborgs are significantly more accurate (up to 35%) than the ResNet, the average participant, and equally-sized groups of humans not assisted by technology. These results suggest that melding humans, BCI, and machine-vision technology could significantly improve decision-making in realistic scenarios.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Adult
  • Brain / physiology
  • Brain-Computer Interfaces*
  • Decision Making*
  • Electroencephalography
  • Evoked Potentials, Visual / physiology
  • Facial Recognition / physiology*
  • Female
  • Healthy Volunteers
  • Humans
  • Male
  • Neural Networks, Computer*
  • Reaction Time / physiology

Grant support

The authors acknowledge support of the UK Defence Science and Technology Laboratory (Dstl) and Engineering and Physical Research Council (EPSRC) under grant EP/P009204/1. This is part of the collaboration between US DOD, UK MOD and UK EPSRC under the Multidisciplinary University Research Initiative. The authors also acknowledge support by the Defence and Security PhD programme through Dstl. Finally, the authors acknowledge support through a collaboration between US DOD, UK MOD under the Bilateral Academic Research Initiative programme (DoD contract W911NF1810434 and UK Dstl contract DSTLX1000128890). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.