Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms
Abstract
This study measures face identification accuracy for an international group of professional forensic facial examiners working under circumstances that apply in real world casework. Examiners and other human face “specialists,” including forensically trained facial reviewers and untrained superrecognizers, were more accurate than the control groups on a challenging test of face identification. Therefore, specialists are the best available human solution to the problem of face identification. We present data comparing state-of-the-art face recognition technology with the best human face identifiers. The best machine performed in the range of the best humans: professional facial examiners. However, optimal face identification was achieved only when humans and machines worked in collaboration.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- May 29, 2018
- Source ID
- 10.1073/pnas.1721355115
Entities
People
- Alice J. O'toole
- Amy N. Yates
- Carina A. Hahn
- Carlos D. Castillo
- David White
- Eilidh Noyes
- Géraldine Jeckeln
- Jacqueline G. Cavazos
- Jun-Cheng Chen
- Kelsey Jackson
- P Jonathon Phillips
- Rajeev Ranjan
- Rama Chellappa
- Swami Sankaranarayanan
- Ying Hu
Organizations
- Intelligence Advanced Research Projects Activity
- National Institute of Justice
- National Institute of Standards and Technology
- University of Maryland
- University of New South Wales
- University of Texas at Dallas