MATEC Web Conf.
Volume 336, 20212020 2nd International Conference on Computer Science Communication and Network Security (CSCNS2020)
|Number of page(s)||7|
|Section||Artificial Recognition and Application|
|Published online||15 February 2021|
Forensic face recognition based on KDE and evidence theory
JiangXi Police College, NanChang, JiangXi, China
* Corresponding author: firstname.lastname@example.org
Forensic face recognition (FFR) has been studied in recent years in forensic science. Given an automatic face recognition system, output scores of the system are used to describe the similarity of face image pairs, but not suitable for forensics. In this study, a score-mapping model based on kernel density estimation (KDE) and evidence theory is proposed. First, KDE was used to generate probability density function (PDF) for each dimensional feature vector of face image pairs. Then, the PDFs could be utilized to determine separately the basic probability assignment (BPA) of supporting the prosecution hypothesis and the defence hypothesis. Finally, the BPAs of each feature were combined by Dempster’s rule to get the final BPA, which reflects the strength of evidence support. The experimental results demonstrate that compared with the classic KDE-based likelihood ratio method, the proposed method has a better performance in terms of accuracy, sensitivity and specificity.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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