Face Verification and Recognition for Digital Forensics and Information Security
Title | Face Verification and Recognition for Digital Forensics and Information Security |
Publication Type | Conference Paper |
Year of Publication | 2019 |
Authors | Amato, Giuseppe, Falchi, Fabrizio, Gennaro, Claudio, Massoli, Fabio Valerio, Passalis, Nikolaos, Tefas, Anastasios, Trivilini, Alessandro, Vairo, Claudio |
Conference Name | 2019 7th International Symposium on Digital Forensics and Security (ISDFS) |
Date Published | jun |
ISBN Number | 978-1-7281-2827-6 |
Keywords | CNN, CNN models, convolutional neural nets, convolutional neural networks, data acquisition, data acquisition process, Deep Learning, deep learning approaches, digital forensics, European COST Action MULTImodal Imaging, face recognition, Face Verification, face verification setup, facial landmarks, FOREnsic SciEnce Evidence, Forensics, Human Behavior, human factors, information forensics, Information security, learning (artificial intelligence), Metrics, MULTIFORESEE, pubcrawl, resilience, Resiliency, Scalability, security, surveillance, verification methods |
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. |
URL | https://ieeexplore.ieee.org/document/8757511 |
DOI | 10.1109/ISDFS.2019.8757511 |
Citation Key | amato_face_2019 |
- Forensics
- verification methods
- surveillance
- security
- Scalability
- Resiliency
- resilience
- pubcrawl
- MULTIFORESEE
- Metrics
- learning (artificial intelligence)
- information security
- information forensics
- Human Factors
- Human behavior
- CNN
- FOREnsic SciEnce Evidence
- facial landmarks
- face verification setup
- Face Verification
- face recognition
- European COST Action MULTImodal Imaging
- Digital Forensics
- deep learning approaches
- deep learning
- data acquisition process
- data acquisition
- convolutional neural networks
- convolutional neural nets
- CNN models