Gender Classification From the Same Iris Code Used for Recognition

@article{Tapia2016GenderCF,
  title={Gender Classification From the Same Iris Code Used for Recognition},
  author={Juan E. Tapia and Claudio A. Perez and Kevin W. Bowyer},
  journal={IEEE Transactions on Information Forensics and Security},
  year={2016},
  volume={11},
  pages={1760-1770}
}
Previous researchers have explored various approaches for predicting the gender of a person based on the features of the iris texture. This paper is the first to predict gender directly from the same binary iris code that could be used for recognition. We found that the information for gender prediction is distributed across the iris, rather than localized in particular concentric bands. We also found that using selected features representing a subset of the iris region achieves better accuracy… CONTINUE READING

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References

Publications referenced by this paper.
SHOWING 1-10 OF 26 REFERENCES

Predicting ethnicity and gender from iris texture

VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Experiments with an improved iris segmentation algorithm

VIEW 4 EXCERPTS

Information Theory and the IrisCode

  • John Daugman
  • Computer Science
  • IEEE Transactions on Information Forensics and Security
  • 2016
VIEW 1 EXCERPT

Feature selection based on mutual information

VIEW 3 EXCERPTS

A review of feature selection methods based on mutual information

VIEW 1 EXCERPT

SVM Based Gender Classification Using Iris Images

VIEW 3 EXCERPTS