On the Fusion of Periocular and Iris Biometrics in Non-ideal Imagery Damon L. Woodard Shrinivas Pundlik Philip Miller Biometrics and Pattern Recognition Lab, School of Computing Clemson University, USA {spundli, pemille, woodard}@clemson.edu Raghavender Jillela Arun Ross Lane Dept. of Computer Science and Electrical Engineering West Virginia University, USA {Raghavender.Jillela, Arun.Ross}@mail.wvu.edu Abstract Human recognition based on the iris biometric is severely impacted when encountering non-ideal images of the eye characterized by occluded irises, motion and spatial blur, poor contrast, and illumination artifacts. This paper discusses the use of the periocular region surrounding the iris, along with the iris texture patterns, in order to improve the overall recognition performance in such images. Periocular texture is extracted from a small, fixed region of the skin surrounding the eye. Ex- periments on the images extracted from the Near Infra- Red (NIR) face videos of the Multi Biometric Grand Challenge (MBGC) dataset demonstrate that valuable information is contained in the periocular region and it can be fused with the iris texture to improve the overall identification accuracy in non-ideal situations. 1 Introduction The human iris exhibits a complicated textural pat- tern on its anterior surface. An iris recognition system exploits the perceived uniqueness of this pattern to dis- tinguish individuals [2]. The key processing steps of an iris recognition system are: (a) acquiring the iris im- agery; (b) locating and segmenting the iris; (c) encod- ing the textural patterns as feature templates; and (d) matching the templates across an existing database for determining identity. A majority of iris recognition sys- tems require a considerable amount of user participa- tion. The iris information captured by the sensor is ei- ther processed immediately, or stored in a database for later processing. The biometric cue resident in an iris image depends on at least two factors: (a) the quality of the image; and (b) the spatial extent of the iris present in the captured image. Both these factors can be regulated at the image acquisition stage to achieve reliable accu- racy. However, such a regulation is possible only when the iris recognition system is employed in an overt situ- ation involving cooperative subjects. Acquiring the iris information becomes extremely challenging in covert operations or in situations involv- ing a non-cooperative subject. Several challenges such as moving subjects, motion blur, occlusions, improper illumination, off-angled irises, specular reflection, and poor image resolution adversely affect the biometric content of the iris data. In such situations, the relia- bility of the iris data could be improved by fusing it with information from the surrounding regions of the eye. Some recent works [9], [11] demonstrate the feasi- bility of using periocular information as a soft biomet- ric trait in high-resolution images of the face. In this work, the feasibility of using periocular biometrics in non-ideal conditions, where iris recognition might not be effective, is studied. 2 Periocular Biometrics A fixed region surrounding the iris of an individual is referred to as the periocular region 1 . Depending on the size of the image used, this region usually encompasses the eyelids, eyelashes, eyebrows, and the neighboring skin area. Using the periocular region has the following advantages: (a) the information regarding the shape of the eye and texture of the skin around it can vary across individuals; which can be used as a soft biometric trait, and (b) no additional sensors, besides the iris camera, are required to acquire the periocular data. Periocular skin texture has been used for human identification in various ways. Jain et al. [6] detect 1 The definition of periocular region provided here is specific to this work. Definitions found in the medical literature can differ from this. 2010 International Conference on Pattern Recognition 1051-4651/10 $26.00 © 2010 IEEE DOI 10.1109/ICPR.2010.58 201 2010 International Conference on Pattern Recognition 1051-4651/10 $26.00 © 2010 IEEE DOI 10.1109/ICPR.2010.58 201 2010 International Conference on Pattern Recognition 1051-4651/10 $26.00 © 2010 IEEE DOI 10.1109/ICPR.2010.58 201 2010 International Conference on Pattern Recognition 1051-4651/10 $26.00 © 2010 IEEE DOI 10.1109/ICPR.2010.58 201 2010 International Conference on Pattern Recognition 1051-4651/10 $26.00 © 2010 IEEE DOI 10.1109/ICPR.2010.58 201