A Comparison of Fused Segmentation Algorithms for Iris Verification

Abstract

Recent studies show fusion at level of segmentation to be useful for more robust iris recognition rates compared with simple segmentation. In this paper we perform Sum-Rule Interpolation at level of the result of the normalized segmented iris images using the well-known Daugman's algorithm, since the process of normalization is essentially composed by two parts: Iris segmentation, in which the pupillary and limbic polar curves are detected and Iris normalization: a normalized representation of the iris texture is created using angular and pupil-tolimbic radial coordinates. For evaluation we propose an experimental fusion scheme using three automatic segmentation algorithms which have reported good results and are not computationally expensive. The experiments were performed on the CASIA V3-Interval, CASIA.V4-Thousand and UBIRIS V1 datasets showing increased recognition accuracy for representative feature extraction algorithms.

A Comparative Study of Robust Segmentation Algorithms for Iris Verification System of High Reliability

“…From capturing one or more images of the iris of a person or persons either for the same type of sensor or multiple sensors, preprocessing of the input image is performed. In this step the inner and outer boundaries of the iris is extracted using at least one of the four previously selected segmentation algorithms implemented [10,17]. Once the iris segmentation was obtained, the transformation of coordinates is performed to obtain the normalized iris image.…”

“…The first step of this segmentation approach [10,11] consists in a rough localization of the pupil area. First, filling the white holes removes specular reflections due to illuminators.…”

“…Contrast-adjusted Hough Transform (CHT), is based on a Masek [10,12] implementation of a Hough Transform approach using (database-specific). It is well known that this method implies a high computational cost.…”

Quality-based iris segmentation-level fusion

“…Segmentation fusion can be grouped into approaches combining detected boundaries prior to any rubbersheet transformation [4,5] and after normalisation, where normalised texture is combined [18,19]. The latter requires multiple execution of the iris unwrapping and normalisation (slower), hiding potential segmentation errors and therefore making their elimination more complex (combination of texture).…”

“…More specifically, in contrast to [18], a single image only is required and unlike [19] normalisation is executed only once. According to our knowledge, this paper is the first to present a quality-driven fusion at segmentationlevel in iris recognition.…”