Like many visual patterns, captured images from the same iris biometric experience relative nonlinear deformations and partial occlusions. These distortions are difficult to normalize for when comparing iris images for match evaluation. We define a probabilistic framework in which an iris image pair constitute observed variables, while parameters of relative deformation and occlusion constitute unobserved latent variables. The relation between these variables are specified in a graphical model, allowing maximum a posteriori probability (MAP) approximate inference in order to estimate the value of the hidden states. To define the generative probability of the observed iris patterns, we rely on the similarity values produced by correlation filter outputs. As a result, we are able to develop an algorithm which returns a robust match metric at the end of the estimation process and works reasonably quickly. We show recognition results on two sets of real iris images: the CASIA database, collected by the Chinese Academy of Sciences, and a database collected by the authors at Carnegie Mellon University.
Correlation filters can be very effective for object recognition. However, these filters may become too computationally expensive when applied to large images, or large numbers of images, because they require Fourier transforms between the spatial and frequency domains. This paper makes the simplifying assumption of single object recognition and presents an algorithm designed to reduce complexity of computation and/or storage. The algorithm derives a frequency domain match metric as opposed to the standard approach of using the spatial correlation plane. The performance of the efficient algorithm is compared to that of the standard correlation filter algorithm, for both accuracy and computational requirements.
Biometric verification refers to the process of matching an input biometric to stored biometric information. In particular, biometric verification refers to matching the live biometric input from an individual to the stored biometric template of that individual. Examples of biometrics include face images, fingerprint images, iris images, retinal scans, etc. Thus, image processing techniques prove useful in biometric recognition. In particular, composite correlation filters have proven to be effective. In this paper, we will discuss the application of composite correlation filters to biometric verification.