| Abstract [eng] |
In this thesis, person identification by combining automatic face and iris recognition is analyzed. Person identification by his face is one of the most intuitive from all biometric measures. We are used to recognizing familiar faces and confirming identity by a short glance at one's id card which contains image of the face. We are also used to being observed by surveillance cameras, which can perform biometric authentication without even being noticed. However, facial biometrics is one of most unstable metrics because the face gets noticeably older in several years and can frequently change depending on the mood of its owner. The core algorithm for facial recognition presented in this work is based on Gabor features. Deep analysis of each step helped to develop the method with better or similar accuracy to the best published results received on the same datasets, while being simple and fast. On the other hand, person identification by his iris is one of the most sophisticated, stable and accurate biometrics. The core algorithm for iris recognition presented in this work is based on a novel iris texture representation by local extremum points of multiscale Taylor expansion. The proposed irises comparison method is very different from the classic phase-based methods, but is also fast and accurate. Combining it with our implementation of phase-based method results in superior recognition accuracy which is comparable or better than any published results received on the same datasets. A combination of aforementioned algorithms was implemented and successfully tested in a recent Multiple Biometrics Grand Challenge Version 2 Portal Challenge experiments, where iris and face videos were captured simultaneously. As expected, recognition accuracy was significantly better when both biometrics were combined. |
