Matlab Code for Iris Segmentation


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Iris segmentation is very important for an iris recognition system. If the iris regions were not correctly segmented, there would possibly exist four kinds of noises in segmented iris regions: eyelashes, eyelids, reflections and pupil, which will result in poor recognition performance. This paper proposes a new noise-removing approach
based on the fusion of edge and region information.


The whole procedure includes three steps: 1) rough localization and normalization, 2) edge information extraction based on phase congruency, and 3) the infusion of edge and region information.



Iris recognition is nowadays considered as one of the most accurate biometric recognition techniques. However, the overall performances of such systems can be reduced in non-ideal conditions, such as unconstrained, on-the-move, or non collaborative setups. In particular, a critical step of the recognition process is the segmentation of the iris pattern in the input face/eye image. This process has to deal with the fact that the iris region of the eye is a relatively small area, wet and constantly in motion due to involuntary eye movements. Moreover, eyelids, eyelashes and reflections are occlusions of the iris pattern that can cause errors in the segmentation process. As a result, an incorrect segmentation can produce erroneous biometric recognitions and seriously reduce the final accuracy of the system.


Segmentation is a critical part in iris recognition systems, since errors in this initial stage are propagated to subsequent processing stages. Therefore, the performance of iris segmentation algorithms is paramount to the performance of the overall system. In order to properly evaluate and develop iris segmentation algorithm, especially under difficult conditions like off-angle and significant occlusions or bad lighting, it is beneficial to directly assess the segmentation algorithm. Currently, when evaluating the performance of iris segmentation algorithms, this is mostly done by utilizing the recognition rate, and consequently the overall performance of the biometric system. However, the overall recognition performance is not only affected by the segmentation accuracy, but also by the performance of the other subsystems based on possible suboptimal segmentation of the iris.


Usually, methods based on a-priori models perform the iris segmentation by searching the edges appertaining to the iris boundaries. However, in the case of degraded or noisy iris images, it can be necessary to use more information in order to obtain accurate results. For this reason, the approaches based on the analysis of local features consider the information related to the iris texture. Moreover, many of these methods do not make any assumptions about the iris shape


Segmentation  process estimates a binary mask  in order to classify the image pixels belonging to the iris pattern. The binary mask excludes occlusions (such as eyelids, eyelashes) and reflections. Additional information can also be extracted, such as the pupil/iris centers and radii, or a secondary binary mask containing the map of the occlusions superimposed on the iris pattern.


IRIS segmentation using Matlab


In order to properly evaluate and compare the performances of iris segmentation methods, it is necessary to use common techniques and figures of merit present in the literature. Also the adoption of public image datasets is very important since it allows to fairly compare the performances of different methods on the same data with the advantage to reduce the efforts necessary to collect new biometric samples.


  • Biometrics
  • Medical imaging
  • Bio medical


Results obtained on public datasets show a good accuracy also for images captured in in non-ideal conditions. However, further studies are necessary in order to obtain applicable results also for images captured in completely unconstrained conditions.


1. International Biometrics Group.

2. ISO/IEC Standard 19794-6. Information technology – biometric data interchange formats, part 6: Iris image data. Tech. rep., International Standards Organization (2005)

3. Aligholizadeh, M.J., Javadi, S., Sabbaghi-Nadooshan, R., Kangarloo, K.: An effective method for eyelashes segmentation using wavelet transform. In: 2011 International Conference on Biometrics and Kansei Engineering (ICBAKE), pp. 185 – 188 (2011)

4. de Almeida, P.: A knowledge-based approach to the iris segmentation problem. Image Vision Comput. 28(2), 238 – 245 (2010)

5. Boles, W., Boashash, B.: A human identification technique using images of the iris and wavelet transform. IEEE Trans. Signal Process. 46(4), 1185 – 1188 (1998)

6. Bowyer, K.W., Flynn, P.J.: The ND-IRIS-0405 Iris Image Dataset. cvrl/papers/ND-Iris-0405.pdf

7. Bowyer, K.W., Hollingsworth, K., Flynn, P.J.: Image understanding for iris biometrics: A survey. Comput. Vis. Image Underst. 110, 281 – 307 (2008)

8. Boykov, Y., Veksler, O.: Graph Cuts in Vision and Graphics: Theories and Applications. In: N. Paragios, Y. Chen, O. Faugeras (eds.) Handbook of Mathematical Models in Computer Vision, chap. 5, pp. 79 – 6. Springer US, New York (2006)


matlab code for iris segmentation


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