Critique: Efficient Iris Recognition by Characterizing Key Local Variations

Size: px

Start display at page:

Download "Critique: Efficient Iris Recognition by Characterizing Key Local Variations"

Marcus Shelton
5 years ago
Views:

1 Critique: Efficient Iris Recognition by Characterizing Key Local Variations Authors: L. Ma, T. Tan, Y. Wang, D. Zhang Published: IEEE Transactions on Image Processing, Vol. 13, No. 6 Critique By: Christopher Boyce Biom693: Advanced Biometrics 04/13/2006

2 1. Introduction and Overview The complex structure of the iris is being evaluated for recognition performance. Since the iris is the only internal organ that can be externally seen on the human body, it can be noninvasively imaged. Due to the iris s epigenetic formation, being formed chaotically in the womb and after birth, it gives rise to randomly distributed feature. This randomness causes the iris features to become highly reliable for personal identification. However, the iris is still difficult to represent effectively, due to the muscular deformations and random features. The paper [1] describes a methodology to perform iris recognition based on the characterization of key local sharp variation points that represent the appearance or disappearance of important image structures. The proposed algorithm extracts the iris features by taking the iris image and characterizing the important information via a set of one dimensional intensity signals, and through using wavelets as a position sequence of local sharp variations to record features. Matching is then performed rapidly on the computed bits using the exclusive OR operator. The performance of the algorithm is then compared to results of the authors [1] iris recognition implementations of Daugman s, Wilde s, Bole s, and Tan s algorithms. 2. Algorithm Review Iris Image Processing Localization The iris is first localized to remove irrelevant parts of the eye structure (e.g. sclera, pupil, and eyelids). Because the pupil is generally darker than the rest of the eye tissue, taking the eye image and taking the minimum of the projection of the image in the vertical and horizontal directions estimates the pupil center

3 coordinates. The center coordinates are then recomputed and binarized using the gray level histogram to attain 120x120 region centered at the initial center coordinates. The centroid of the binary region is taken as the new center pupil coordinates of the image. Center coordinates of the pupil can still be misplaced by this technique and therefore be recomputed in this 120x120 binarized fashion to detect the exact center points. Edge detection is then performed using the canny operator and the Hough transform in a region given by the pupil centroid. Normalization Due to the nature of iris being a muscle and able to constrict and dilate, and the iris acquisition system used to image the iris, various registration problems occur across image captures. The iris structure can have less pixels within the captured image with the elastic dilation and different camera position, camera distances, and head orientations can cause the iris to appear larger or radially shifted. Therefore the iris is unwrapped, counter clockwise, to a rectangular texture block representing polar coordinates. This normalization slightly reduces the elastic distortions of the iris. Enhancement Because of the inherent low contrast and nonuniform brightness present in the image, in the near infrared spectrum, normalization is done. Intensity variations across the segmented iris image are approximated to obtain a proportionally distributed texture image. A block size of 16x16 is taken and the mean is computed to get a coarse estimate of the illumination in the background. Bicubic interpolation is then done to expand the background illumination estimation to the

4 same size as the normalized image. Subtracting the resized background illumination from the normalized image then compensates for the illumination changes. Next, 32x32 pixel regions are selected and histogram equalized. This improves the contrast between structural components of the iris block. Feature Extraction 1-D Intensity Signal Generation Because the iris is a sphincter muscle its details exhibit a radial similarity. The rows of the 2-D iris block image correspond to this radial similarity. The 2-D normalized image is decomposed into a set of 1-D intensity signals. Each intensity signal is a combination of successive horizontal scan lines. Theses lines reflect local variations of an object along the horizontal direction and a set of 1-D signals conveys the majority of the local sharp variations contained within the iris structure. Because structural variations near the sclera are typically minimal and are usually occluded by eyelashes and eyelids in some manner, the top 78% (near the pupil) of the 2-D iris is taken. The total number of 1-D signals used in the experiment in [1] is ten and the number of rows used to form the signal is five. Feature Vector The dyadic wavelet is used to decompose a signal into detail components appearing at different scales. The dyadic wavelets parameter varies only along the dyadic sequence. The local sharp variations can be precisely located using the dyadic wavelet in this way. These variations indicate the appearance and disappearance of an important iris structure. Using local extreme, minimum or maximum, detection the sharp iris variation points can be located. Where the

5 local maximum denotes the vanishing of an irregular block and the local minimum denotes the appearing of an irregular block. A small block may be present if adjacent local extreme points occur. It can happen, however, that a few adjacent local extreme points between which the amplitude difference is small. These extreme points correspond to faint iris characteristics that are undesirable for recognition purposes. Therefore, thresholding is done by the amplitude difference to suppress these points. The intensity signals are then concatenated to form a corresponding feature vector of the local sharp variation points in the intensity signal, where the first scale correspond to the first scale and the next components to the other scale. The local sharp variation point is set to 1 if the first local sharp variation point is a local minimum of the wavelet transform and 1 otherwise. All 1-D intensity signals are then concatenated to constitute an order feature vector, varying across irises because of size differences. The differences between feature components is then computed with the actual position of local sharp variation points minus the previous position of local sharp variation points to save on storage memory. Matching Two irises are determined to be of the same class by a comparison of the feature vectors, using a Daugman [2] like X-OR operation. The original feature is expanded into a binary feature vector. If the feature is a local maximum point of the wavelet transform the binary sequence is set to 1, otherwise the binary sequence is set to 0. The X-OR operation, matching of bits equals 1, is then performed for fast determination of similarity. The bits are then shifted for

6 matching because of rotation of the iris image. This brute force technique determines the best match of the binary feature vectors. Experimental Results A data set of 213 subjects comprised mainly of Chinese origin was collected, probably in the near IR range. A comparative study of iris recognition algorithms is then conducted to evaluate performance. The iris recognition algorithms in [2] [3] [4] were implemented by the authors of [1], and verification and identification experiments for each algorithm are compared. No eyelash or eyelid detection was performed in the authors implementation. Daugman s algorithm and the authors proposed algorithm performed the highest for the data set, whereas Boles s and Tan s algorithms were slightly less accurate in terms of EER and correct recognition rate. A time comparison was also done with Bole s algorithm taking the least time to perform recognition. However, all methods took less than a second to execute. 3. Critique Localization The localization technique used to determine the initial center coordinates of the iris from the minimum of the pixel values and the gray level histogram of a block region is a good initial estimation. However, this technique has to be iterated several times to find the exact coordinats of the pupil. This could be done simply by histogram thresholding the original image to include the dark potions of the eye image, corresponding mainly to the pupil and possible the eyelashes, and using morphological operators to determine the exact pupil area. Using this area a circle or ellipse can be fit to find the center and radial coordinates of the pupil.

7 This is tends to be faster, dependent on the coding implementation, than using the Hough transform to find the circular regions. The Hough transform is computationally complex and even in the reduced search space given in [1] will take a longer time to compute. Also, before the Hough transform is applied the edges are detected using the canny edge detector. Other edge detectors could be used or a detector that is biased in the vertical direction could be used to properly detect the important edge features, corresponding to the sides of the iris and pupil, in the iris image. Normalization Due to the various types of registration transformation that can be present in the image and the elastic deformation that is caused by different illumination the iris has to be normalized before matching can be performed. The paper ([1]) transforms the iris into a polar coordinate system making each radial circular ring of the iris a row of the new image. In this way slight deformation caused by illumination changes are compensated for. However, this is not true for heavily dilated or constricted irides. The polar coordinate system cannot compensate for extreme elastic deformation. One possible way to account for the deformation is to define a set of control points in a dilated image and a constricted image, and through using thin plate splines the deformation can be modeled more precisely than the polar coordinate system. However, the polar coordinate system does make subsequent processing and matching techniques very easy. The unwrapping could cause undesirable distortions. So, the image does not necessarily have to be unwrapped it could be register as a circular entity, as Wildes does, and processed subsequently in a circular fashion.

8 Enhancement Because of the variant illumination and the inherent poor contrast caused when imaging the iris, due to the shape of the face, the occlusion of the eyelids and the nonuniformity of the illumination source, the illumination pattern must be normalized and enhanced. The papers method of illumination estimation and block wise histogram equalization for compensating for these illumination problems is very efficient and should work well to counter the effects of variant illumination problems and enhance contrast. However, binarizing the image or possibly taking only the edge structures of the image using the canny operator to enhance the image could further enhance contrast. Feature Extraction 1-D Intensity Signal Generation Because of the radial nature of the iris, the structures within the iris can be taken row wise in the horizontal direction of the 2-D image, and the local sharp variations along this direction in the normalized image can characterize the iris structures in 1 dimension. This is a very fast technique for feature extraction. However, it simplifies discriminate variations that are present in the iris that could be exploited to better classify a certain iris. Instead the entire 2-D texture could be used as part of the feature vector, but this will increase the computational cost of the algorithm. Also [1] states, the iris regions close to the sclera contain few texture characteristics and are easy to be occluded by the eyelashes and eyelids. Therefore we extract features only in the top-most 78% section of the normalized iris. This is true in most brown irides but there are quite a few exceptions where

9 there is rich structure corresponding to the outer portions of the iris near the sclera. Also, blue irides structure tends to be more radially distributed throughout the iris. So, they are in affect discarding more discriminate structure information in this reduction. They could instead segment the exact iris boundary and use it as a mask to exclude the occlusion and eyelash portion of the eye. Feature Vector The dyadic wavelet is used to decompose the 1-D signal into detail components appearing at different scales. This wavelet is a very effective means (computationally) of determining these variations in the 1_D signal though the local extremes. This feature extraction could be done using other parameterizations of the 1-D signal, but there would probably be a similar final performance. Also, the paper sets a threshold to use only the prominent characteristics in the iris image represented by the 1-D structure. This again eliminates certain textural features that could be utilized to discriminate between irides. Matching To perform matching the feature vector of the local extremes is converted to a binary sequence via the local maximum equaling 1 and the 0 otherwise. This greatly reduces the computational cost of the system while slightly decreasing the discriminatory nature of the iris. Matching of the actual local extremes could produce higher inter class distance however intra class distance could become more variable due to the complex deformations and transformations of the iris. The X-OR operator is used to match the computed binary sequence after pixel

10 translation shifting the iris to account for circular rotational shifts caused by head or camera orientations. The matching performance is computed for each circular shift. This is a brute force approach to determine the circular shift. The shift approach could be simplified by using a defined block size initially to determine an approximate alignment, and then proceed to do the necessary pixel shifting. The circle shift, however, is still not going to compensate for the elastic deformation of the iris muscle. Experimental Results The data set collect to test the algorithms implemented by the authors is composed of 213 subjects with images taken from each eye. The subject population is 95% Chinese greatly conforming the dataset to the brown highly occlude nature of a Chinese eye. Therefore, variations and structures present in Caucasian blue eyes and other races are not taken into consideration when evaluating the algorithms. Also, there is very slight color and structural information correlation across one subject s left and right eye. This could lead to an increase in the false match rate if both irides are used in the testing. Also, during the performance evaluation the authors evaluate the speed and performance of other algorithms compared to there own. It should be noted that it is difficult to do a comparison of another algorithm without having the actual implementation tweaked to fit the dataset it is to be tested on. Although this does test the robustness of the algorithms, but is the authors representation of the actual implementation. So, the timing and the performance could be construed worse or even better than the original algorithm. The authors also bring up the

11 nature of time lag between capturing sessions and the slight performance degradation that occurs. The capture time shouldn t have a large affect on the actual iris structure, but the conditions such as lighting and head orientation will change. This should also be exhibited if the system and the user were slightly moved between captures. So, the actual length of time between captures shouldn t matter, unless the time span is vary large, e.g. a decade. References 1. L. Ma, T. Tan, Y. Wang, D. Zhang, Efficient Iris Recogntion by Characterizing Key Local Variations. IEEE transactions on Image Processing, Vol. 13, No. 6, June J. Daugman, High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Analy. Machine Intell., vol. 15 pp , Nov R. Wildes, et al, A machine vision system for iris recognition, Mach. Vis. Applic., Vol 9, pp1-8, W. Boles, B. Boashash, A human identification technique using images of the iris and wavelet transform, IEEE Trans. Signal Processing, vol. 46. pp , Apr. 1998

An Efficient Iris Recognition Using Correlation Method

, pp. 31-40 An Efficient Iris Recognition Using Correlation Method S.S. Kulkarni 1, G.H. Pandey 2, A.S.Pethkar 3, V.K. Soni 4, &P.Rathod 5 Department of Electronics and Telecommunication Engineering, Thakur