الفهرس 
Chapter (1) Introduction and ObjectivesOnly 14 pages are availabe for public view
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Abstract
Iris has been widely recognized as one of the strongest biometrics attributed to
the high system performance of iris recognition systems. However, templates in
conventional iris recognition systems are unprotected and highly vulnerable to
numerous security and privacy attacks. A number of iris template protection schemes
have been proposed, but at the expense of substantially decreased system performance
from the security perspective. In this dissertation, we introduce new cancelable iris
template protection schemes. Instead of using original iris features, masked versions
of these features are generated for increasing the iris recognition system privacy. The
proposed work will be generally divided into five main schemes. These proposed
schemes strike the balance between system performance and privacy/security
protection. Moreover, evaluation metrics are used for the performance of the proposed
schemes. The dissertation objectives will be summarized separately in the following
paragraphs.
Firstly, we present a random projection scheme for cancelable iris recognition.
Instead of using original iris features, masked versions of the features are generated
through the random projection in order to increase the security of the iris recognition
system. The proposed framework for iris recognition includes iris localization, sector
selection of the iris to avoid eyelids and eyelashes effects, normalization, segmentation of normalized iris region into halves, selection of the upper half for further reduction
of eyelids and eyelashes effects, feature extraction with Gabor filter, and finally
random projection. This framework guarantees exclusion of eyelids and eyelashes
effects, and masking of the original Gabor features to increase the level of security.
Matching is performed with the Hamming Distance (HD) metric. The proposed
framework achieves promising recognition rates and a leading Equal Error Rate (EER).
Secondly, a chaos-based cancelable biometric scheme for iris recognition is
proposed. The chaotic map encryption is used for generating cancelable IrisCodes for
increasing system privacy. A modification of the Logistic map is included to increase the key space, and hence the privacy is enhanced. The encryption key depends on the
input image. Hence, the resultant encrypted feature vector is sensitive to the key. Thus,
the encrypted feature vector is robust as the key space is large. This proposed scheme
achieves a high accuracy and a good EER upon using the modified Logistic map on
the CASIA-IrisV3 dataset.
Thirdly, we implement the optical Double Random Phase Encoding (DRPE)
algorithm in cancelable face and iris recognition systems. In the proposed cancelable
face recognition scheme, the Scale Invariant Feature Transform (SIFT) is used for
feature extraction from the face images. The extracted feature map is encrypted with
the DRPE algorithm. On the other hand, the proposed cancelable iris recognition
system depends on the utilization of two iris images for the same person. Features are
extracted from both images. The features extracted from one of the iris images are
encrypted with the DRPE algorithm provided that the second phase mask used in the
DRPE algorithm is generated from the other iris image features. This trend guarantees
some sort of feature fusion between the two iris images into a single cancelable iris
code and increases privacy of users. Simulation results show a good performance of
the two proposed cancelable biometric schemes even in the presence of noise, especially with the proposed cancelable face recognition scheme.
Fourthly, we present a new technique for cancelable iris recognition using
Comb filtering approach. In this technique, all enrollment patterns are masked using a
transformation function, and the invertibility process for obtaining the original data
should not be possible. Experimental results are con are have been calculated for
different values of Comb filter orders and compared with the unprotected IrisCode
results. Hamming distance and Receiver Operating characteristic (ROC) distributions
are estimated for different Comb filter orders to check the system robustness and
stability. The experimental results achieve a significant gain for both privacy and
performance proving the superiority of the proposed scheme. Also, the proposed
scheme achieves a high accuracy and a promising EER.Lastly, we present a novel CNN model that successfully classifies different
scenarios of cancelable biometric traits using a bio-convolving method applied on both
image and feature levels. On the contrary of most conventional secure recognition
systems, the proposed scheme for cancelable biometric recognition maintains high
accuracy results, while maintaining cancelability. The performance metrics are
evaluated for different traits with different datasets; LFW, FERET, IITD, and CASIAIrisV3.
The experimental results are demonstrated for each database and are compared
with those of the state-of-the-art methods applied to the same databases. The results
show the robustness and effectiveness of the proposed scheme. Moreover, it shows
high recognition rates for all databases.