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Open Set face recognition using transduction.

Fayin Li1, Harry Wechsler

  • 1Department of Computer Science, George Mason University, Fairfax, VA 22030, USA. fli@cs.gmu.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|November 16, 2005
PubMed
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This study introduces Open Set TCM-kNN for face recognition, enabling systems to identify known individuals or reject unknown ones. This novel approach enhances security by handling open-set scenarios effectively.

Area of Science:

  • Computer Science
  • Biometrics
  • Machine Learning

Background:

  • Open Set face recognition challenges systems to identify known individuals while rejecting unknown ones.
  • Existing methods struggle with scenarios where test probes may not have corresponding gallery entries.

Purpose of the Study:

  • To introduce a novel transductive inference method for Open Set face recognition.
  • To present Open Set TCM-kNN (Transduction Confidence Machine-k Nearest Neighbors) for multiclass authentication with rejection capabilities.

Main Methods:

  • Utilizing transductive inference and Kolmogorov complexity for likelihood ratio estimation.
  • Developing Open Set TCM-kNN for local estimation in detection tasks.
  • Applying Pattern Specific Error Inhomogeneities (PSEI) analysis for error structure investigation.

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Main Results:

  • Demonstrated feasibility, robustness, and advantages of Open Set TCM-kNN on Open Set identification and watch list tasks using FERET data.
  • Showcased suitability of Open Set TCM-kNN for PSEI analysis to identify difficult-to-recognize faces.
  • Confirmed PSEI analysis improves biometric performance by addressing error-causing face patterns.

Conclusions:

  • Open Set TCM-kNN effectively addresses the Open Set face recognition task, including rejection of unknown identities.
  • The method provides a robust solution for multiclass authentication scenarios with an "none of the above" option.
  • PSEI analysis integrated with Open Set TCM-kNN enhances biometric system performance by targeting specific error patterns.