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Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
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Kernelized Heterogeneity-Aware Cross-View Face Recognition.

Tejas I Dhamecha1, Soumyadeep Ghosh1, Mayank Vatsa2

  • 1IIIT Delhi, New Delhi, India.

Frontiers in Artificial Intelligence
|August 6, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces heterogeneous discriminant analysis (HDA) and its kernel version (KHDA) for improved cross-view face matching. These methods effectively reduce heterogeneity variance, achieving state-of-the-art results in diverse facial recognition scenarios.

Keywords:
cross-resolutioncross-spectraldiscriminant analysis (DA)face recognition (FR)heterogeneity

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biometrics

Background:

  • Heterogeneous face matching compares faces across different modalities like spectrum or resolution.
  • Existing methods struggle with significant variations between face views.

Purpose of the Study:

  • To develop novel subspace techniques for robust heterogeneous face matching.
  • To introduce a face recognition framework leveraging these new techniques.

Main Methods:

  • Proposed heterogeneous discriminant analysis (HDA) and kernel heterogeneous discriminant analysis (KHDA).
  • These methods encode heterogeneity into the objective function to create an invariant projection space.
  • Applied HDA/KHDA to existing facial features within a proposed face recognition framework.

Main Results:

  • HDA and KHDA demonstrated effectiveness across visible-to-near-infrared, cross-resolution, and photo-to-sketch matching.
  • Consistent reduction in heterogeneity variance was observed, leading to improved recognition accuracy.
  • Achieved state-of-the-art or comparable results against recent heterogeneous matching algorithms.

Conclusions:

  • HDA and KHDA are effective for heterogeneous face recognition.
  • The proposed framework achieves high accuracy, including 99.4% rank-1 on CASIA NIR-VIS 2.0 and 100% on CMU Multi-PIE.
  • These techniques offer a significant advancement in handling cross-view face variations.