Face Verification and Recognition for Digital Forensics and Information Security

Amato G., Falchi F., Gennaro C., Massoli F. V., Passalis N., Tefas A., Trivilini A., Vairo C.

Forensics  Surveillance  Face Verification  Security  Deep Learning 

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.

Source: 7th International Symposium on Digital Forensics and Security (ISDFS 2019), Barcelos, Portugal, 10/6/2019, 12/6/2019

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