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Energy normalization for pose-invariant face recognition based on MRF model image matching.

Shervin Rahimzadeh Arashloo1, Josef Kittler

  • 1Center for Vision, Speech and Signal Processing, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, Surrey GU27XH, UK. sr00048@surrey.ac.uk

IEEE Transactions on Pattern Analysis and Machine Intelligence
|December 8, 2010
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the C=O, C=N, and C=C occur between 1600–1850 cm−1.
The...

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This study introduces a novel pose-invariant face recognition system using Markov Random Fields (MRFs). The system accurately matches faces despite moderate pose variations without needing extensive preprocessing or training on non-frontal images.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biometrics

Background:

  • Face recognition systems often struggle with variations in pose, requiring extensive preprocessing.
  • Existing methods may necessitate training on diverse facial poses, increasing complexity.

Purpose of the Study:

  • To develop a pose-invariant face recognition system that is robust to moderate spatial transformations.
  • To eliminate the need for explicit geometric preprocessing and training on non-frontal face images.

Main Methods:

  • Utilizes an image matching method formulated on Markov Random Fields (MRFs).
  • Employs the energy of image matches as a measure of goodness-of-match.
  • Incorporates a registration step within the system to handle pose variations dynamically.

Related Experiment Videos

Main Results:

  • The system demonstrates tolerance to moderate global spatial transformations between images.
  • Achieved favorable comparisons against existing 2D and 3D generative model-based methods.
  • Successfully evaluated on XM2VTS (verification) and CMU-PIE (identification) databases.

Conclusions:

  • The proposed MRF-based method offers an effective solution for pose-invariant face recognition.
  • Innovations like dynamic block adaptation and error pre-whitening enhance system performance.
  • The system provides a viable alternative to traditional face recognition approaches, particularly in scenarios with pose variations.