Iris Recognition Approach for Preserving Privacy in Cloud Computing

Biometric identification systems involve securing biometric traits by encrypting them using an encryption algorithm and storing them in the cloud. In recent decades, iris recognition schemes have been considered one of the most effective biometric models for identifying humans based on iris texture, due to their relevance and distinctiveness. The proposed system focuses on encrypting biometric traits. The user’s iris feature vector is encrypted and stored in the cloud. During the matching process, the user’s iris feature vector is compared with the one stored in the cloud. If it meets the threshold conditions, the user is authenticated. Iris identification in cloud computing involves several steps. First, the iris image is pre-processed to remove noise using the Hough transform. Then, the pixel values are normalized, Gabor filters are applied to extract iris features. The features are then encrypted using the AES 128-bit algorithm. Finally, the features of the test image are matched with the stored features on the cloud to verify authenticity. The process ensures the privacy and security of the iris data in cloud storage by utilizing encryption and efficient image processing techniques. The matching is performed by setting an appropriate threshold for comparison. Overall, the approach offers a significant level of safety, effectiveness, and accuracy

Investigation of Multimodal Template-Free Biometric Techniques and Associated Exception Handling

The Biometric systems are commonly used as a fundamental tool by both government and private sector organizations to allow restricted access to sensitive areas, to identify the criminals by the police and to authenticate the identification of individuals requesting to access to certain personal and confidential services. The applications of these identification tools have created issues of security and privacy relating to personal, commercial and government identities. Over the last decade, reports of increasing insecurity to the personal data of users in the public and commercial domain applications has prompted the development of more robust and sound measures to protect the personal data of users from being stolen and spoofing. The present study aimed to introduce the scheme for integrating direct and indirect biometric key generation schemes with the application of Shamir‘s secret sharing algorithm in order to address the two disadvantages: revocability of the biometric key and the exception handling of biometric modality. This study used two different approaches for key generation using Shamir‘s secret sharing scheme: template based approach for indirect key generation and template-free. The findings of this study demonstrated that the encryption key generated by the proposed system was not required to be stored in the database which prevented the attack on the privacy of the data of the individuals from the hackers. Interestingly, the proposed system was also able to generate multiple encryption keys with varying lengths. Furthermore, the results of this study also offered the flexibility of providing the multiple keys for different applications for each user. The results from this study, consequently, showed the considerable potential and prospect of the proposed scheme to generate encryption keys directly and indirectly from the biometric samples, which could enhance its success in biometric security field

Multimodal Biometric Systems for Personal Identification and Authentication using Machine and Deep Learning Classifiers

Multimodal biometrics, using machine and deep learning, has recently gained interest over single biometric modalities. This interest stems from the fact that this technique improves recognition and, thus, provides more security. In fact, by combining the abilities of single biometrics, the fusion of two or more biometric modalities creates a robust recognition system that is resistant to the flaws of individual modalities. However, the excellent recognition of multimodal systems depends on multiple factors, such as the fusion scheme, fusion technique, feature extraction techniques, and classification method. In machine learning, existing works generally use different algorithms for feature extraction of modalities, which makes the system more complex. On the other hand, deep learning, with its ability to extract features automatically, has made recognition more efficient and accurate. Studies deploying deep learning algorithms in multimodal biometric systems tried to find a good compromise between the false acceptance and the false rejection rates (FAR and FRR) to choose the threshold in the matching step. This manual choice is not optimal and depends on the expertise of the solution designer, hence the need to automatize this step. From this perspective, the second part of this thesis details an end-to-end CNN algorithm with an automatic matching mechanism. This thesis has conducted two studies on face and iris multimodal biometric recognition. The first study proposes a new feature extraction technique for biometric systems based on machine learning. The iris and facial features extraction is performed using the Discrete Wavelet Transform (DWT) combined with the Singular Value Decomposition (SVD). Merging the relevant characteristics of the two modalities is used to create a pattern for an individual in the dataset. The experimental results show the robustness of our proposed technique and the efficiency when using the same feature extraction technique for both modalities. The proposed method outperformed the state-of-the-art and gave an accuracy of 98.90%. The second study proposes a deep learning approach using DensNet121 and FaceNet for iris and faces multimodal recognition using feature-level fusion and a new automatic matching technique. The proposed automatic matching approach does not use the threshold to ensure a better compromise between performance and FAR and FRR errors. However, it uses a trained multilayer perceptron (MLP) model that allows people’s automatic classification into two classes: recognized and unrecognized. This platform ensures an accurate and fully automatic process of multimodal recognition. The results obtained by the DenseNet121-FaceNet model by adopting feature-level fusion and automatic matching are very satisfactory. The proposed deep learning models give 99.78% of accuracy, and 99.56% of precision, with 0.22% of FRR and without FAR errors. The proposed and developed platform solutions in this thesis were tested and vali- dated in two different case studies, the central pharmacy of Al-Asria Eye Clinic in Dubai and the Abu Dhabi Police General Headquarters (Police GHQ). The solution allows fast identification of the persons authorized to access the different rooms. It thus protects the pharmacy against any medication abuse and the red zone in the military zone against the unauthorized use of weapons

Biometrics: Effectiveness and Applications within the Blended Learning Environment

Learning methods have been benefited by a large act of recent systems based on the merging of several models of teaching. Blended learning philosophy has undergone a deep change with the internalization of new engineering sciences such as biometric. While it is known that passwords or PIN should never be stored in the clear, biometric technologies are becoming the foundation of an all-inclusive array of highly secure identification and personal verification solutions. In this paper, we present an in depth discussion the effectiveness of applying different types of biometrics in blended learning environments. We outline an implementation and report the effectiveness of the fingerprint model as a secure biometric method on a database consisting of 13000 students. Keywords: Blended learning, biometrics, e-learning, fingerprint matching, information technology

A Survey on Modality Characteristics, Performance Evaluation Metrics, and Security for Traditional and Wearable Biometric Systems

Biometric research is directed increasingly towards Wearable Biometric Systems (WBS) for user authentication and identification. However, prior to engaging in WBS research, how their operational dynamics and design considerations differ from those of Traditional Biometric Systems (TBS) must be understood. While the current literature is cognizant of those differences, there is no effective work that summarizes the factors where TBS and WBS differ, namely, their modality characteristics, performance, security and privacy. To bridge the gap, this paper accordingly reviews and compares the key characteristics of modalities, contrasts the metrics used to evaluate system performance, and highlights the divergence in critical vulnerabilities, attacks and defenses for TBS and WBS. It further discusses how these factors affect the design considerations for WBS, the open challenges and future directions of research in these areas. In doing so, the paper provides a big-picture overview of the important avenues of challenges and potential solutions that researchers entering the field should be aware of. Hence, this survey aims to be a starting point for researchers in comprehending the fundamental differences between TBS and WBS before understanding the core challenges associated with WBS and its design