Title: A New Texture Analysis Approach for Iris Recognition

Writer: IzemHamouchene, SalihaAouat

Technical Implementation

There are few steps in iris recognition systems. The steps are image acquisition,

iris preprocessing, feature extraction and also the matching steps as shown in figure

below (Hamouchene & Aouat, 2014).

Note *Due to the two ring of the iris are not co-centric, Integro-differential

operator by Daugman is being used to detect the inner and outer boundaries

(Daugman, 2004).

**Daugman using Hamming distance and threshold around 0.34

Figure: Typical process of iris recognition

Figure: Conversion of iris image to iris code

A novel new feature extraction method known as Neighborhood-Binary Pattern

(NBP) is being proposed (Hamouchene & Aouat, 2014). This method is inspired by

Local Binary Pattern (LBP) method as NBP method are able to capture local

information and in the same time iris texture can be describe better.

LBP method which proposed by Ojala and Pietikainen is using analysis window

with the size of 3x3. This method is basically comparing each neighborhood pixels

with values of the central pixel and following conditions are given where if

neighborhood pixel value is above or equal to central pixel value, then it is encoded

with value of 1, otherwise the neighborhood pixel value is encoded with 0. After

being threshold by the central pixel values, a binary code can be obtained and this

binary code will be converted to decimal number.

For NBP method, the neighborhood pixel values are being threshold as in LBP

but the difference is NBP method is comparing the neighborhood pixel values with

their respective next neighbor instead of central pixel values. The conditions are about

the same where a value of 1 is given if its gray value (neighborhood pixel value) is

greater than the next neighbor otherwise value of 0 is given.

Figure: Extraction of NBP pattern

Discussion

By picking one the pixel value in the 3x3 analysis window (except for central

pixel value), the value is start comparing with adjacent values to determine 0 or 1. The

binary code is further converted to decimal value. If a small rotation happened to the

analysis window, different NBP code (binary code) will be obtained.

In order to prevent the rotation problem, an encoding process is being proposed.

This encoding process with pick the largest neighborhood pixel value and start

compare it with adjacent neighborhood pixel value. This encoding process can result

in the same binary code even though rotation on the analysis is happened.

Figure: Rotation invariant for NBP method.

A way to describe the NBP image is by using decomposing architecture. This

method will firstly divide the image into several blocks where mean value for each

block is being calculated and its variations will be encoded. Same as the condition

used in NBP, if value of one block is bigger than its neighbor block, a value of 1 will

be given and 0 otherwise. Therefore, we will obtain a binary matrix of the variation

means and we can use it as the template of the iris texture.

Figure: Process of encoding mean variation

Intersection method is being used for the matching purpose by comparing the iris

images. Similarity distances between the two extracted matrices are being calculated

by performing below equation:

As show in the above equation, M1 and M2 are the variation binary codes for the

iris images. S value for the ith block is equal to 1 if the value of M1 for ith block is

equal to the value of M2 for ith block. Nb is representing the total number of blocks

and this value is based on the degree of decomposition of the iris image. If value of

Dis is above certain threshold, the two iris images (1 and 2) are referred as same

person.

Public iris database, CASIA is being used to evaluate the performance of the

system. As suggest by (Hamouchene & Aouat, 2014), three images from each person

are taken as reference and 80 images will be used as test images where each image are

referred as query. For each of the image, LBP histogram and mean variation of the

NBP image are being extracted. Between the query’s feature and extracted features,

hamming distance is being calculated. By sorting the hamming distance from most

similar to less similar, the top three is being considered and the query iris is classified

by referring the majority (highest similarity).

Figure: Recognition process flow

Result

Figure: Recognition rate for LBP and NBP method for each person

From the above graph, we can see that NBP method is way better than the LBP

method as the LBP’s global rate is only 58.75% where NBP’s rate is 76.25%. This is

because of NBP method is comparing the neighborhood pixel values with its adjacent

pixel values instead of being threshold by the central pixel values. In other words, we

can say there is relationship among the neighborhood pixel values for NBP method.

This result had shown the robustness and efficiency of the NBP method as compared

with LBP method.

Conclusion

We can conclude that NBP method is having good performance as compared with

LBP. This is because of there is relative connection between the neighborhood pixels

as each one of them is being thresholded by the adjacent neighbor and encoded. Not

only that, NBP image is being decomposed into number of blocks where their

variation of mean values are being extracted and encoded. This will result the binary

matrix being used as the feature descriptor for the iris image.

References

Hamouchene, I., & Aouat, S. (2014). A New Texture Analysis Approach for Iris

Recognition. AASRI Procedia, 9, 2-7.

Daugman, J. (2004). How iris recognition works. Circuits and Systems for Video

Technology, IEEE Transactions on, 14(1), 21-30.

CASIA iris image database (v1.0), The National Laboratory of Pattern Recognition

(NLPR), Institute of Automation, Chinese Academy of Sciences (CAS), 2006