Fusion of Face and Iris Biometrics

The practice of using more than one biometric modality, sample, sensor, or algorithm to achieve recognition, commonly referred to as multi-biometrics, is a technique that is rapidly gaining popularity. By incorporating multi-biometrics into the recognition process, many of the short-comings of traditional single-biometric systems can be alleviated and overall recognition accuracy can be improved. Multibiometrics can inherently increase system robustness by removing the dependency on one particular biometric approach. Further, a system that utilizes more than one biometric feature or matcher may be more difficult to deliberately spoof [17]. Systems that make use of multiple biometric features can also provide redundancy that may lower failure-to-acquire rates. Though multi-biometrics offers many potential advantages over traditional biometric systems, inefficient system design can greatly increase sensor cost, computation time, and data acquisition time. While research into multi-biometrics has received a large increase in attention over recent years, the task of fusing multiple biometric modalities from a single sensor remains an under-studied challenge. Due to a lack of available multi-modal data, many current experiments in multi-biometrics create “chimeric” datasets, in which samples of one biometric modality from one set of subjects are arbitrarily paired with a second biometric modality from a separate set of subjects in order to simulate a multi-biometric scenario [1]. This approach, though useful for preliminary experimentation, may mask unknown dependencies between modalities. Further, chimeric datasets simulate a multi-biometric scenario in which samples of each modality are acquired independently. In practice, it is much more desirable to simultaneously acquire multiple modalities from a single sensor if possible for cost and usability reasons.