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Periocular-based soft biometric classification

Periocular-based soft biometric classification

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Soft biometric traits refer to characteristics that provide some information about an individual but do not possess the distinctiveness and permanence necessary to sufficiently differentiate between any two individuals. Examples of soft biometric traits include gender, ethnicity, age, weight, and height. As early as 1997, researchers suggested that soft biometrics could be used to improve biometric recognition performance [1]. Researchers later demonstrated the use of gender, ethnicity, and height to improve the performance of a fingerprint recognition system [2]. This chapter discusses the use of local appearance features extracted from the periocular region for gender and ethnicity classification.

Chapter Contents:

  • 9.1 Introduction
  • 9.2 Approach
  • 9.2.1 Data
  • 9.2.2 Preprocessing
  • 9.2.2.1 Geometric normalization
  • 9.2.2.2 Histogram equalization
  • 9.2.2.3 Periocular region extraction
  • 9.2.3 Feature representations
  • 9.2.3.1 Local binary patterns
  • 9.2.3.2 Local ternary patterns
  • 9.2.3.3 Local salient patterns
  • 9.2.3.4 Local phase quantization
  • 9.2.3.5 Local color histograms
  • 9.2.3.6 Histogram of Gabor ordinal measures
  • 9.2.4 Classification
  • 9.3 Experiment results
  • 9.3.1 Experiment protocol
  • 9.3.2 Gender classification
  • 9.3.3 Ethnicity classification
  • 9.3.4 Gender and ethnicity classification
  • 9.4 Summary
  • References

Inspec keywords: feature extraction; image classification

Other keywords: biometric recognition performance improvement; ethnicity classification; fingerprint recognition system; height classification; periocular-based soft biometric classification; age classification; weight classification; gender classification; local appearance feature extraction

Subjects: Computer vision and image processing techniques; Image recognition

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