A biometric system provides automatic recognition of an individual based on some sort of unique feature or characteristic possessed by the individual. Biometric systems have been developed based on fingerprints, facial features, voice, hand geometry, handwriting, the retina], and in this case with the iris.
Biometric systems work by first capturing a sample of the feature, such as recording a digital sound signal for voice recognition, or taking a digital colour image for face recognition. The sample is then transformed using some sort of mathematical function into a biometric template. The biometric template will provide a normalised, efficient and highly discriminating representation of the feature, which can then be objectively compared with other templates in order to determine identity. Most biometric systems allow two modes of operation. An enrolment mode for adding templates to a database, and an identification mode, where a template is created for an individual and then a match is searched for in the database of pre-enrolled templates.
Figure 1: Iris Recognition Block Diagram
Whenever people log onto computers, access an ATM, pass through airport security, use credit cards, or enter high-security areas, they need to verify their identities. Thus, there is tremendous interest in improved methods for reliable and secure identification of people. The main block diagram for iris recognition has five stage:
- Iris acquisition Stage:
Figure 2:Top: IrisGuard AD-100 Sensor. Below: Example of captured images
- Segmented Stage:
Figure 3: These images show two segmented irises and the masked information in red.
- Normalized Stage
Figure 4: Normalize images with the mask in yellow.
- Encoding Stage
Figure 5: Encoding images using gabor filters.
Today, we can get more information from the Iris images. Therefore Soft biometrics involves the estimation from iris of Age, Ethnicity, Gender and maybe others. Gender classification based on iris images is currently one of the most challenging problems in image analysis research. In a biometric recognition framework, gender classification can help by requiring a search of only half of the subjects in the database.