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Face detection based on a human attention guided multi-scale model.

Marinella Cadoni1, Andrea Lagorio2, Enrico Grosso2

  • 1Dipartimento di Scienze Biomediche, Università di Sassari, Viale San Pietro 43B, 07100, Sassari, Italy. maricadoni@uniss.it.

Biological Cybernetics
|December 1, 2023
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Summary
This summary is machine-generated.

Human attention, specifically eye fixations, can improve multiscale models for face detection. This approach helps select optimal spatial scales and areas of interest for better performance.

Keywords:
Attention-guided modelFacial visual attentionMultiscale face detectionMultiscale face model

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Human-Computer Interaction

Background:

  • Multiscale models, like Deformable Part-based Models (DPMs), are advanced techniques for face detection and recognition.
  • Current DPMs rely on heuristic-based, expert-defined part configurations and scales, lacking biological inspiration.

Purpose of the Study:

  • To investigate if human visual attention, specifically fixation patterns, can inform the design of multiscale models for face detection.
  • To determine if incorporating human fixation data can optimize the selection of spatial scales and feature areas.

Main Methods:

  • Utilizing a multiscale pyramid representation to extract salient points from facial images.
  • Employing human fixation data to guide the selection of relevant points and scales within the multiscale representation.

Main Results:

  • Demonstrated that a multiscale pyramid can effectively extract key points for face analysis.
  • Showed that human attention patterns can successfully identify scales and areas leading to superior face detection performance.

Conclusions:

  • Human fixations offer a biologically plausible and effective method for optimizing multiscale models in computer vision.
  • This research provides a novel approach to enhance face detection by integrating principles of human visual attention.