Ali Ismail Awad

Evaluation of Acceleration Algorithm for Biometric Identification

This paper evaluates an existing acceleration algorithm for biometric identification. In identification based on biometric images, the number of image comparisons is an important factor to estimate the total processing time in addition to the processing time of a single image comparison. Maeda et al. proposed an identification algorithm which reduces the number of image comparisons. This paper evaluates the algorithm in terms of the time and the accuracy with the features extracted by SIFT from palmprint images. The evaluation in this paper proves that the algorithm is applicable to the SIFT-based palmprint features. However, the evaluation also proves that an overhead of the algorithm requires the processing time which depends on the database size. Therefore, for an identification system with a large database, the total processing time of an identification is not reduced by a straightforward application of the algorithm by Maeda et al.

Evaluation of a Fingerprint Identification Algorithm with SIFT Features

In fingerprint identification, the short identification time is a crucial need. The identification time can be estimated according to the number of conducted matching processes multiplied by the consumed time by a single matching process. The Matching Score Matrix is an existing identification algorithm that reduces the number of matching processes. This paper evaluates the algorithm with the features extracted from fingerprint images by SIFT. The processing time and the accuracy are compared with those of the straightforward method based on the linear search. The evaluation results prove the applicability of the algorithm to fingerprint identification using SIFT features.

Fingerprint Singularity Detection: A Comparative Study

A singular point or singularity on fingerprint is considered as a fingerprint landmark due its scale, shift, and rotation immutability. It is used for both fingerprint classification and alignment in automatic fingerprint identification systems. This paper presents a comparative study between two singular point detection methods available in the literature. The Poincare index method is the most popular approach, and the complex filter is another proposed method applied on the complex directional images. The maximum complex filter response is highly related to the regions with abrupt changes in the ridge orientations. These regions have a high probability to contain a singular point. The optimum detection method in both processing time and detection accuracy will be updated to suite our efficient classification method. The experimental evaluation for both methods proves that the accuracy achieved by complex filter is up to 95% with considerable processing time compared to 90% with Poincare index method.

Advances in Security of Information and Communication Networks: First International Conference, SecNet 2013, Cairo, Egypt, September 3-5, 2013. Proceedings

This book constitutes the refereed proceedings of the International Conference on Advances in Security of Information and Communication Networks, Sec Net 2013, held in Cairo, Egypt, in September 2013. The 21 revised full papers presented were carefully reviewed and selected from 62 submissions. The papers are organized in topical sections on networking security; data and information security; authentication and privacy; security applications.

Toward An Efficient Fingerprint Classification

Biometrics technology is keep growing substantially in the last decades with great advances in biometric applications. An accurate personal authentication or identification has become a critical step in a wide range of applications such as national ID, electronic commerce, and automated and remote banking. The recent developments in the biometrics area have led to smaller, faster, and cheaper systems such as mobile device systems. As a kind of human biometrics for personal identification, fingerprint is the dominant trait due to its simplicity to be captured, processed, and extracted without violating user privacy. In a wide range of applications of fingerprint recognition, including civilian and forensics implementations, a large amount of fingerprints are collected and stored everyday for different purposes. In Automatic Fingerprint Identification System (AFIS) with a large database, the input image is matched with all fields inside the database to identify the most potential identity. Although satisfactory performances have been reported for fingerprint authentication (1:1 matching), both time efficiency and matching accuracy deteriorate seriously by simple extension of a 1:1 authentication procedure to a 1:N identification system (Manhua, 2010). The system response time is the key issue of any AFIS, and it is often improved by controlling the accuracy of the identification to satisfy the system requirement. In addition to developing new technologies, it is necessary to make clear the trade-off between the response time and the accuracy in fingerprint identification systems. Moreover, from the versatility and developing cost points of view, the trade-off should be realized in terms of system design, implementation, and usability. Fingerprint classification is one of the standard approaches to speed up the matching process between the input sample and the collected database (K. Jain et al., 2007). Fingerprint classification is considered as indispensable step toward reducing the search time through large fingerprint databases. It refers to the problem of assigning fingerprint to one of several pre-specified classes, and it presents an interesting problem in pattern recognition, especially in the real and time sensitive applications that require small response time. Fingerprint classification process works on narrowing down the search domain into smaller database subsets, and hence speeds up the total response time of any AFIS. Even for

A Cattle Identification Approach Using Live Captured Muzzle Print Images

Cattle identification receives a great research attention as a dominant way to maintain the livestock. The identification accuracy and the processing time are two key challenges of any cattle identification methodology. This paper presents a robust and fast cattle identification approach from live captured muzzle print images with local invariant features. The presented approach compensates some weakness of traditional cattle identification schemes in terms of accuracy and processing time. The proposed scheme uses Scale Invariant Feature Transform (SIFT) for detecting the interesting points for image matching. In order to enhance the robustness of the presented technique, a Random Sample Consensus (RANSAC) algorithm has been coupled with the SIFT output to remove the outlier points and achieve more robustness. The experimental evaluations prove the superiority of the presented approach because it achieves 93.3% identification accuracy in reasonable processing time compared to 90% identification accuracy achieved by some other reported approaches.

Impact of Some Biometric Modalities on Forensic Science

Recently, forensic science has had many challenges in many different types of crimes and crime scenes, vary from physical crimes to cyber or computer crimes. Accurate and efficient human identification or recognition have become crucial for forensic applications due to the large diversity of crime scenes, and because of the increasing need to accurately identify criminals from the available crime evidences. Biometrics is an emerging technology that provides accurate and highly secure personal identification and verification systems for civilian and forensic applications. The positive impact of biometric modalities on forensic science began with the rapid developments in computer science, computational intelligence, and computing approaches. These advancements have been reflected in the biometric modality capturing process, feature extraction, feature robustness, and features matching. A complete and automatic biometric identification or recognition systems have been built accordingly. This chapter presents a study of the impacts of using some biometric modalities in forensic applications. Although biometrics identification replaces human work with computerized and automatic systems in order to achieve better performance, new challenges have arisen. These challenges lie in biometric system reliability and accuracy, system response time, data mining and classification, and protecting user privacy. This chapter sheds light on the positive and the negative impacts of using some biometric modalities in forensic science. In particular, the impacts of fingerprint image, facial image, and iris patterns are considered. The selected modalities are covered preliminarily before tackling their impact on forensic applications. Furthermore, an extensive look at the future of biometric modalities deployment in forensic applications is covered as the last part of the chapter.

Biometrics Applications in e-Health Security : A Preliminary Survey

Driven by the desires of healthcare authorities to offer better healthcare services at a low cost, electronic Health (e-Health) has revolutionized the healthcare industry. However, while e-Health comes with numerous advantages that improve health services, it still suffers from security and privacy issues in handling health information. E-Health security issues are mainly centered around user authentication, data integrity, data confidentiality, and patient privacy protection. Biometrics technology addresses the above security problems by providing reliable and secure user authentication compared to the traditional approaches. This study explores the security and privacy issues in e-Health, and offers a comprehensive overview of biometrics technology applications in addressing the e-Health security challenges. The paper concludes that biometrics technology has considerable opportunities for application in e-Health due to its ability to provide reliable security solutions. Although, additional issues like system complexity, processing time, and patient privacy related to the use of biometrics should be taken into consideration.

Business and Government Organizations’ Adoption of Cloud Computing

Cloud computing is very much accepted and acknowledged worldwide as a promising computing paradigm. On-demand provisioning based on a pay-per-use business model helps reduce costs through sharing computing and storage resources. Although cloud computing has become popular, some business and government organizations are still lagging behind in adopting cloud computing. This study reports the status of cloud utilization among business and government organizations, and the concerns of organizations regarding the adoption of cloud computing. The study shows that some government agencies are lagging behind in cloud computing use, while others are leading the way. Security is identified as the major reason for delay in adopting cloud computing. The outcomes of the data analysis process prove that some security measures such as encryption, access authentication, antivirus protection, firewall, and service availability are required by clients for adoption of cloud computing in the future.

Bio-inspiring Cyber Security and Cloud Services: Trends and Innovations

This volume presents recent research in cyber security and reports how organizations can gain competitive advantages by applying the different security techniques in real-world scenarios. The volume provides reviews of cuttingedge technologies, algorithms, applications and insights for bio-inspiring cyber security-based systems. The book will be a valuable companion and comprehensive reference for both postgraduate and senior undergraduate students who are taking a course in cyber security. The volume is organized in self-contained chapters to provide greatest reading flexibility.