Analyzing the impact of police mugshots in face verification for crime investigations

Contardo, Paolo; Lorenzo, Emanuele Di; Falcionelli, Nicola; Dragoni, Aldo Franco; Sernani, Paolo

doi:10.1109/metroxraine54828.2022.9967671

Cited by 1 publication

(2 citation statements)

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“…To this end, this paper extends our previous work [ 24 ] by proposing the Face Recognition from Mugshot Database (FRMDB), a dataset of face images and videos to test the use of mugshot pictures taken from multiple Points of View (POVs), as reference images in the face recognition on video surveillance frames. The proposed dataset can be used to measure the accuracy of face recognition with different subsets of mugshot pictures.…”

Section: Introductionmentioning

confidence: 92%

“…The database is available upon request, with a dedicated release agreement. Given its features, the SCFace database was the only one available to test the capability of CNNs to perform face recognition on surveillance images using different subsets of mugshots, as we did in our previous research [ 24 ]. Nevertheless, all the pictures from the security cameras are almost frontal, whereas, in real life, a subject might be framed from different perspectives.…”

Section: Literature Reviewmentioning

confidence: 99%

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FRMDB: Face Recognition Using Multiple Points of View

Contardo

Sernani

Tomassini

et al. 2023

Sensors

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Although face recognition technology is currently integrated into industrial applications, it has open challenges, such as verification and identification from arbitrary poses. Specifically, there is a lack of research about face recognition in surveillance videos using, as reference images, mugshots taken from multiple Points of View (POVs) in addition to the frontal picture and the right profile traditionally collected by national police forces. To start filling this gap and tackling the scarcity of databases devoted to the study of this problem, we present the Face Recognition from Mugshots Database (FRMDB). It includes 28 mugshots and 5 surveillance videos taken from different angles for 39 distinct subjects. The FRMDB is intended to analyze the impact of using mugshots taken from multiple points of view on face recognition on the frames of the surveillance videos. To validate the FRMDB and provide a first benchmark on it, we ran accuracy tests using two CNNs, namely VGG16 and ResNet50, pre-trained on the VGGFace and VGGFace2 datasets for the extraction of face image features. We compared the results to those obtained from a dataset from the related literature, the Surveillance Cameras Face Database (SCFace). In addition to showing the features of the proposed database, the results highlight that the subset of mugshots composed of the frontal picture and the right profile scores the lowest accuracy result among those tested. Therefore, additional research is suggested to understand the ideal number of mugshots for face recognition on frames from surveillance videos.

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Section: Introductionmentioning

confidence: 92%

Section: Literature Reviewmentioning

confidence: 99%