A multi-texture approach for estimating iris positions in the eye using 2.5D Active Appearance Models

This paper describes a new approach for the detection of the iris center. Starting from a learning base that only contains people in frontal view and looking in front of them, our model (based on 2.5D Active Appearance Models (AAM)) is capable of capturing the iris movements for both people in frontal view and with different head poses. We merge an iris model and a local eye model where holes are put in the place of the white-iris region. The iris texture slides under the eye hole permitting to synthesize and thus analyze any gaze direction. We propose a multi-objective optimization technique to deal with large head poses. We compared our method to a 2.5D AAM trained on faces with different gaze directions and showed that our proposition outperforms it in robustness and accuracy of detection specifically when head pose varies and with subjects wearing eyeglasses.