Evaluating OpenFace: an open-source automatic facial comparison algorithm for forensics

• Main Authors: Angeliki Fydanaki (Author), Zeno Geradts (Author)
• Format: Book
• Published: Oxford University Press, 2018-07-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2096-1790; 2471-1411; 10.1080/20961790.2018.1523703
• Keywords: Forensic sciences; digital forensic; face comparison; OpenFace; deep learning; Criminal law and procedure; K5000-5582; Public aspects of medicine; RA1-1270
• Source: Forensic Sciences Research, Vol 3, Iss 3, Pp 202-209 (2018)

This article studies the application of models of OpenFace (an open-source deep learning algorithm) to forensics by using multiple datasets. The discussion focuses on the ability of the software to identify similarities and differences between faces based on images from forensics. Experiments using OpenFace on the Labeled Faces in the Wild (LFW)-raw dataset, the LFW-deep funnelled dataset, the Surveillance Cameras Face Database (SCface) and ForenFace datasets showed that as the resolution of the input images worsened, the effectiveness of the models degraded. In general, the effect of the quality of the query images on the efficiency of OpenFace was apparent. Therefore, OpenFace in its current form is inadequate for application to forensics, but can be improved to offer promising uses in the field.