Gender classification from near infrared iris images

Gender classification from near infrared iris images

For access to this article, please select a purchase option:

Buy chapter PDF
(plus tax if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
Iris and Periocular Biometric Recognition — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Gender classification is an important topic in a wide variety of applications ranging from surveillance to selective marketing. Several recent studies have shown the predominance of local matching approaches in gender classifications results. Previous works in predicting gender-from-iris have relied on computing a separate set of textures representation. The state of the art shows that gender can be successfully predicted from the iris. There are clear computational advantages to predicting gender from the binary iris-code rather than computing another different texture representation. This topic brings new insights about the information present in the iris (and iris-code) to determine demographic information. The previous work adds evidence answering the fundamental question that the iris contains specific information about us, such as gender. The results, which show that gender classification from iris code is possible, will spur research to determine if other demographic factors (e.g., ethnicity, age, emotions) can also be predicted. This is an area of research that is overall in the early stages.

Chapter Contents:

  • 8.1 Introduction
  • 8.2 Anatomy structure of the eye
  • 8.3 Feature extraction
  • 8.4 State of the art
  • 8.5 Databases
  • 8.5.1 BioSecure multimodal database
  • 8.5.2 Gender from iris dataset (ND-GFI)
  • 8.6 Feature selection
  • 8.7 Research trends and challenges
  • 8.7.1 Segmentation
  • 8.7.2 Accuracy
  • 8.7.3 Fragile bits
  • 8.7.4 Sensors
  • 8.7.5 Makeup
  • 8.8 Concluding remarks
  • Acknowledgments
  • References

Inspec keywords: image classification; infrared imaging; iris recognition

Other keywords: demographic factors; age factor; gender classification; binary iris-code; emotion factor; near infrared iris images; ethnicity factor

Subjects: Image recognition; Computer vision and image processing techniques

Preview this chapter:
Zoom in

Gender classification from near infrared iris images, Page 1 of 2

| /docserver/preview/fulltext/books/sc/pbse005e/PBSE005E_ch8-1.gif /docserver/preview/fulltext/books/sc/pbse005e/PBSE005E_ch8-2.gif

Related content

This is a required field
Please enter a valid email address