Personal Identification by Integrating a Number of Features from Iris and Periocular Region Using AdaBoost
Shintaro Oishi, Yoshihiro Shirakawa, Masatsugu Ichino, Hiroshi Yoshiura · Journal of Information Processing · 2018
A personal identification method has been developed for searching for a specific person among other people that fuses iris and periocular features using AdaBoost. It effectively integrates scores for many features using an AdaBoost configuration in which feature selection corresponds to weak classifier selection. We found three interesting facts of evaluation. First, evaluation using up to eight features showed that identification accuracy increased with the number of features used. The lowest equal error rate (EER) was 1.3% when eight features were used, and the highest identification rate was 94.1% when eight features were used. Second, the advantage of the proposed method over a weighted sum method increased with the number of features used. The difference in EER was 1.1% when eight features were used due to the generation of a nonlinear decision boundary, and the difference in the identification rate was 1.8% when eight features were used, again due to the generation of a nonlinear decision boundary. Finally, using an effective combination of information from both eyes further improved the accuracy (the difference in EER between the four-feature case and the eight-feature case was 0.7%, and the difference in identification rate between the four-feature case and the eight-feature case was 4.6%).