On the reproducibility and repeatability of likelihood ratio in forensics: A case study using face biometrics

Suki, Nik; Poh, Norman; Senan, Firham M.; Zamani, Nazri Ahmad; Darus, Maslina

doi:10.1109/btas.2016.7791192

Cited by 3 publications

(1 citation statement)

References 25 publications

(30 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…But these methods need to be manually carried out by forensic experts, so they heavily depend on the experience and knowledge of the forensic experts. On the other hand, the objectivebased methods attempt to identify faces using automatic face recognition [6][7][8][9][10]. Using automatic recognition system to verify faces can not only improve the efficiency of forensic work, but also promote the standardization of the forensic process.…”

Section: Introductionmentioning

confidence: 99%

Forensic face recognition based on KDE and evidence theory

Wen

2021

MATEC Web Conf.

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%