Phd thesis on iris recognition

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Texture code matrix-based multi-instance iris recognition

Shibboleth OpenAthens. Some features of this site may not work without it. My Account Login Register. Help How to submit and FAQs. Show simple item record Contributions to practical iris biometrics on smartphones dc. Iris recognition is a mature and widely deployed technology which will be able to provide the high security demanded by next generation smartphones.

  • Esbern Andersen-Hoppe defended his Master's Thesis on 'Iris Recognition on mobile Devices';
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Practical challenges in widely adopting this technology on smartphones are identified. Based on this, a number of design strategies are presented for constraint free, high performing iris biometrics on smartphones. In this way, the lost pixels are filled with nearest valid pixel in the same row or column though it may not be nearest neighbor. In other words, the separable SAWT cannot always find the nearest neighbor, which it may be in the other rows or columns. However, non-separability of Gabor-wavelet provides an opportunity to find the nearest neighbor more effectively.

Therefore, for having shape adaptive Gabor-wavelet, we fill the noisy pixels in each acceptable block from view of Eq. Then, the Gabor-wavelet is applied to enhanced image block and iris codes are obtained by using Eq.

Where C i and M i are iris code and mask code, respectively. The proposed scheme is evaluated in this section. The iris samples were captured under NIR lighting. First session contains images from individuals while in the second session; only individuals have been participated to capture images. We prefer to use these databases because they contain many noisy iris images due to occlusions by eyelids, eyelashes and reflections.

On the other hand, each iris classes should have at least four images, three for enrollment and others for recognition. Thus, only iris images from classes for CASIA-IrisV3-Interval database and iris images images from classes for the first session and images from classes for second session can be used in our experiments. Figure 6 shows some localized iris images from these two databases. As can be seen, the SDM parameters are almost same for both databases and in turn, SDM is compatible with any database.

Figure 7 illustrates visual effect of proposed noise-removing approach on the normalized iris images. However, as can be seen in Figure 7 , the proposed noise-removing approach copes to detect them effectively. Biometric systems work in two modes: Identification e. Correct recognition rate CRR is used to test the identification mode as the ratio of the number of correct classifications to the total test samples.

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The iris codes are generated using the information of LH and HL sub-bands in the fourth level and therefore, the length of iris codes and mask codes is bits. The Gabor filter bank which is used here has four filters that the Gabor parameters selection is done by reported values in our earlier work. Therefore, the iris codes and mask codes contain bits. The k parameter in Eq. Large values for k produce more reduction in the number of effective bits to be compared. The reliability of the obtained results is enhanced by more participated bits in the matching step. The performance comparisons for wavelet transform and Gabor-Wavelet feature extraction including with and without shape adaptive are provided in Table 1.

In addition, to evaluate the performance improvement by proposed noise-removing approach, we present a system without noise-removing step i. The recognition accuracy is compared by ROC curves in Figure 9. The value of k is considered 0. Visual effect illustrations of proposed noise-removing approach. Left column shows before applying noise-removing and right column shows after applying noise-removing.

EEL6825 Iris Recognition

Comparison equal error rate and correct recognition rate results for various values of k. Receiver operating characteristic curves for wavelet transform and Gabor-Wavelet feature extraction including with and without shape adaptive and noise-removing on the database. Using shape adaptive transforms for iris feature extraction does not make a significant improvement while it imposes complexity on the system especially for shape adaptive Gabor-Wavelet due to multi directional boundary extension.

This comes from the boundary extension in SAWT, which is done in one direction, horizontal or vertical. Gabor-wavelet decomposes the textures in more directions four directions here while the wavelet transform decomposes the textures only in vertical and horizontal directions. Hence, Gabor-wavelet feature extraction produces more distinction. Moreover, one can optimize the Gabor parameters to reach best accuracy while this is not possible in wavelet feature extraction.

In addition, proposed encoding for extracted features by Gabor-Wavelet is compatible with iris texture variations as it causes to decrease the recognition error. Due to extracting the features from high level sub-bands of decomposed iris textures by wavelet transform, the length of iris codes is short. However, this is not considered as an advantage. In fact, we observed using the information of low level sub-bands to generate the iris codes decreases the system performance because of accompanying noise with high frequency components.

Therefore, to reduce the influence of noise on the results, low frequency sub-bands are used to generate the iris codes. Using low signature size in noisy environments produces decrease in recognition accuracy. Furthermore, the advantages of using masks disappear when the iris code length is short.

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This is the reason of degradation in recognition rate for wavelet features including masks against a system without masks for second session of UBIRIS database, which is captured in very noisy environment. An effective feature extraction algorithm should be robust against segmentation errors.

This is why that the recognition rate of extracted features by wavelet transform degrades more than Gabor features on CASIA database.

Tuning Iris Recognition for Noisy Images | SpringerLink

This shows that the robustness of wavelet feature extraction is less than Gabor-wavelet feature extraction. In order to compare the proposed scheme with existing methods, we try to find the state-of-the-art approaches that were implemented using the iris databases employed in this paper. We rarely find the papers, which were considered both aforementioned databases.

Therefore, separate tables are provided for each database. Sun and Tan[ 18 ] used multilobe differential filters for ordinal iris feature extraction. The best performance is for combining the di-lobe and tri-lobe for nonlocal ordinal codes, which is brought in Table 2. Tsai et al. Then, they used a filter bank including 12 Gabor filters corresponding to two frequencies and six orientations to extract 96 local real-valued feature points.

The matching strategy was based on fuzzy clustering algorithm. Their system was tested for identification and verification mode, which the results can be seen in Table 2. Masek[ 3 ] detected the eyelids using linear Hough transform and the eyelashes using a constant threshold.

Ma et al. Rathgeb et al.

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In Table 2 , we use the reported values for Masek's and Ma's method by Rathgeb et al. Tajbakhsh et al. For each iris image, they generated five iris codes and then used SVM-based fusion rule for decision making. Pinheiro et al. Chen et al. These numerical evaluations show the proposed method for Gabor-Wavelet feature extraction provides a significant improvement on the recognition rate especially for UBIRIS database while we use more images than the others. Using the accurate localization algorithms such as active contours and level set, it can make more improvements on the performance and we will concentrate on this step in our future endeavors.

In this paper, we investigated the effect of shape adaptive transforms to exclude the noisy pixels in feature extraction step. Experimental results demonstrate the shape adaptive transform cannot make a significant improvement on the recognition rate. This owes to the novel noise-removing approach proposed in this paper.

crowducgalin.gq It effectively detects eyelids, eyelashes, reflections, out of framework, pupil and sclera as noise factors in the normalized iris images. The novelty is to detect eyelashes and reflections through finding appropriate thresholds by a procedure called SDM. In addition, an accurate and fast iris localization approach based on coarse-to-fine strategy is presented.

Furthermore, we elaborate how the mask codes are generated to exclude the noisy bits in an iris code. Literature reviews show there are not any details about the mask code generation. Hamed Ghodrati recieved the B. His research interests include biometric especially iris recognition, watermarking, data hiding, image processing and pattern recognition.

E-mail : moc. His activities have included Image signal processing, digital filter structures, filter banks, wavelet based signal processing and wireless communication. E-mail : ri. Habibolah Danyali received the B.