Name
Abstract | ||
|---|---|---|
In this paper, we present an extensive evaluation of face recognition and verification approaches performed by the European COST Action MULTI-modal Imaging of FOREnsic SciEnce Evidence (MULTI-FORESEE). The aim of the study is to evaluate various face recognition and verification methods, ranging from methods based on facial landmarks to state-of-the-art off-the-shelf pre-trained Convolutional Neural Networks (CNN), as well as CNN models directly trained for the task at hand. To fulfill this objective, we carefully designed and implemented a realistic data acquisition process, that corresponds to a typical face verification setup, and collected a challenging dataset to evaluate the real world performance of the aforementioned methods. Apart from verifying the effectiveness of deep learning approaches in a specific scenario, several important limitations are identified and discussed through the paper, providing valuable insight for future research directions in the field. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ISDFS.2019.8757511 | 2019 7th International Symposium on Digital Forensics and Security (ISDFS) |
Keywords | DocType | ISBN |
Forensics,Face Verification,Deep Learning,Surveillance,Security | Conference | 978-1-7281-2828-3 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giuseppe Amato | 1 | 505 | 106.68 |
| Fabrizio Falchi | 2 | 459 | 55.65 |
| Claudio Gennaro | 3 | 490 | 57.23 |
| Fabio Valerio Massoli | 4 | 11 | 3.98 |
| N. Passalis | 5 | 117 | 33.70 |
| Anastasios Tefas | 6 | 2055 | 177.05 |
| Alessandro Trivilini | 7 | 0 | 0.34 |
| Claudio Vairo | 8 | 97 | 11.35 |