Cancelable Iris Template Generation Using Weber Local Descriptor and Median Filter Projection
Ilaiah Kavati, Venkatesh Akula, Erukala Suresh Babu, Ramalingaswamy Cheruku · 2023
In recent years, the growing use of biometric recognition systems in various applications has increased the need to protect the biometric templates recorded in multiple databases. Due to their consistency and uniqueness, iris recognition systems have significantly outperformed other biometrics. Directly stored Iris templates on a central server constitute a privacy and security risk. To address this, we will generate a cancelable template that can be stored instead of the original. In the event of a security breach, we will discard the stored template and generate a new iris template. This research employs the Weber Local Descriptor (WLD) technique to create a multi-instance iris biometric system. Left and right iris images are initially acquired and normalized using the USIT toolkit. We generate a feature vector from the normalized image using WLD. The obtained feature vector is then normalized using L1 normalization. The vector of normalized features is then projected onto a median filter to generate a cancelable template. Experiments are conducted on the IIT Delhi iris database, and the results are optimistic compared to previously published research.