CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2014 vol.36 Issue No.01 - Jan.
Issue No.01 - Jan. (2014 vol.36)
Jaishanker K. Pillai , Dept. of Electr. & Comput. Eng., Univ. of Maryland, College Park, MD, USA
Maria Puertas , Dept. of Electr. & Comput. Eng., Univ. of Maryland, College Park, MD, USA
Rama Chellappa , Dept. of Electr. & Comput. Eng., Univ. of Maryland, College Park, MD, USA
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.98
Due to the increasing popularity of iris biometrics, new sensors are being developed for acquiring iris images and existing ones are being continuously upgraded. Re-enrolling users every time a new sensor is deployed is expensive and time-consuming, especially in applications with a large number of enrolled users. However, recent studies show that cross-sensor matching, where the test samples are verified using data enrolled with a different sensor, often lead to reduced performance. In this paper, we propose a machine learning technique to mitigate the cross-sensor performance degradation by adapting the iris samples from one sensor to another. We first present a novel optimization framework for learning transformations on iris biometrics. We then utilize this framework for sensor adaptation, by reducing the distance between samples of the same class, and increasing it between samples of different classes, irrespective of the sensors acquiring them. Extensive evaluations on iris data from multiple sensors demonstrate that the proposed method leads to improvement in cross-sensor recognition accuracy. Furthermore, since the proposed technique requires minimal changes to the iris recognition pipeline, it can easily be incorporated into existing iris recognition systems.
Iris recognition, Sensors, Kernel, Optimization, Training, Joints,biometrics, Sensor shift, cross-sensor matching, Kernel learning, adaptation, iris
Jaishanker K. Pillai, Maria Puertas, Rama Chellappa, "Cross-Sensor Iris Recognition through Kernel Learning", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.36, no. 1, pp. 73-85, Jan. 2014, doi:10.1109/TPAMI.2013.98