We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
FRMDB: Face Recognition Using Multiple Points of View.
- Authors
Contardo, Paolo; Sernani, Paolo; Tomassini, Selene; Falcionelli, Nicola; Martarelli, Milena; Castellini, Paolo; Dragoni, Aldo Franco
- Abstract
Although face recognition technology is currently integrated into industrial applications, it has open challenges, such as verification and identification from arbitrary poses. Specifically, there is a lack of research about face recognition in surveillance videos using, as reference images, mugshots taken from multiple Points of View (POVs) in addition to the frontal picture and the right profile traditionally collected by national police forces. To start filling this gap and tackling the scarcity of databases devoted to the study of this problem, we present the Face Recognition from Mugshots Database (FRMDB). It includes 28 mugshots and 5 surveillance videos taken from different angles for 39 distinct subjects. The FRMDB is intended to analyze the impact of using mugshots taken from multiple points of view on face recognition on the frames of the surveillance videos. To validate the FRMDB and provide a first benchmark on it, we ran accuracy tests using two CNNs, namely VGG16 and ResNet50, pre-trained on the VGGFace and VGGFace2 datasets for the extraction of face image features. We compared the results to those obtained from a dataset from the related literature, the Surveillance Cameras Face Database (SCFace). In addition to showing the features of the proposed database, the results highlight that the subset of mugshots composed of the frontal picture and the right profile scores the lowest accuracy result among those tested. Therefore, additional research is suggested to understand the ideal number of mugshots for face recognition on frames from surveillance videos.
- Subjects
HUMAN facial recognition software; FACE perception; VIDEO surveillance; DATABASES
- Publication
Sensors (14248220), 2023, Vol 23, Issue 4, p1939
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s23041939