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FRMDB: Face Recognition Using Multiple Points of View.

Paolo Contardo1,2, Paolo Sernani3, Selene Tomassini1

  • 1Dipartimento di Ingegneria dell'Informazione, Università Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy.

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Summary
This summary is machine-generated.

This study introduces the Face Recognition from Mugshots Database (FRMDB) for improving surveillance video face recognition. Findings show multiple mugshot viewpoints enhance accuracy, challenging traditional single-view methods.

Keywords:
face identificationface verificationlaw enforcementpolice mugshotsvideo surveillance

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Area of Science:

  • Computer Science
  • Biometrics
  • Artificial Intelligence

Background:

  • Face recognition technology faces challenges with arbitrary poses in real-world applications like surveillance.
  • Existing research lacks comprehensive datasets for face recognition using multiple mugshot viewpoints against surveillance footage.

Purpose of the Study:

  • To introduce the Face Recognition from Mugshots Database (FRMDB) to address the scarcity of data for multi-view mugshot face recognition.
  • To analyze the impact of using diverse mugshot Points of View (POVs) on face recognition accuracy in surveillance videos.

Main Methods:

  • Developed the FRMDB with 28 mugshots and 5 surveillance videos across 39 subjects.
  • Utilized Convolutional Neural Networks (CNNs), VGG16 and ResNet50, pre-trained on VGGFace and VGGFace2, for feature extraction.
  • Compared performance against the Surveillance Cameras Face Database (SCFace).

Main Results:

  • The FRMDB provides a benchmark for multi-view face recognition in surveillance.
  • CNN models achieved varying accuracies depending on the mugshot set composition.
  • Using only frontal and profile mugshots yielded the lowest accuracy among tested configurations.

Conclusions:

  • The FRMDB facilitates research into optimizing face recognition with varied mugshot data.
  • Multi-view mugshots significantly impact recognition accuracy in surveillance scenarios.
  • Further research is needed to determine the optimal number and types of mugshots for robust face recognition.