Implementation of AES using biometric

Mobile Adhoc network is the most advanced emerging technology in the field of wireless communication. MANETs mainly have the capacity of self-forming, self-healing, enabling peer to peer communication between the nodes, without relying on any centralized network architecture. MANETs are made applicable mainly to military applications, rescue operations and home networking. Practically, MANET could be attacked by several ways using multiple methods. Research on MANET emphasizes on data security issues, as the Adhoc network does not befit security mechanism associated with static networks. This paper focuses mainly on data security techniques incorporated in MANET. Also this paper proposes an implementation of Advanced Encryption Standard using biometric key for MANETs. AES implementation includes, the design of most robust Substitution-Box implementation which defines a nonlinear behavior and mitigates malicious attacks, with an extended security definition. The key for AES is generated using most reliable, robust and precise biometric processing. In this paper, the input message is encrypted by AES powered by secured nonlinear S-box using finger print biometric feature and is decrypted using the reverse process

A new algorithm for minutiae extraction and matching in fingerprint

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A novel algorithm for fingerprint template formation and matching in automatic fingerprint recognition has been developed. At present, fingerprint is being considered as the dominant biometric trait among all other biometrics due to its wide range of applications in security and access control. Most of the commercially established systems use singularity point (SP) or ‘core’ point for fingerprint indexing and template formation. The efficiency of these systems heavily relies on the detection of the core and the quality of the image itself. The number of multiple SPs or absence of ‘core’ on the image can cause some anomalies in the formation of the template and may result in high False Acceptance Rate (FAR) or False Rejection Rate (FRR). Also the loss of actual minutiae or appearance of new or spurious minutiae in the scanned image can contribute to the error in the matching process. A more sophisticated algorithm is therefore necessary in the formation and matching of templates in order to achieve low FAR and FRR and to make the identification more accurate. The novel algorithm presented here does not rely on any ‘core’ or SP thus makes the structure invariant with respect to global rotation and translation. Moreover, it does not need orientation of the minutiae points on which most of the established algorithm are based. The matching methodology is based on the local features of each minutiae point such as distances to its nearest neighbours and their internal angle. Using a publicly available fingerprint database, the algorithm has been evaluated and compared with other benchmark algorithms. It has been found that the algorithm has performed better compared to others and has been able to achieve an error equal rate of 3.5%

Study of some fingerprint verfication algorithms

Fingerprint Verification is one of the most reliable personal identification methods. Image processing provides power tools for this purpose. The target can be mainly decomposed into image preprocessing, feature extraction and feature match. For each sub-task, some classical and up-to-date methods in literatures are analyzed. Based on the analysis, an integrated solution for fingerprint recognition is implemented

Skeleton-based fingerprint minutiae extraction.

by Zhao Feng.Thesis (M.Phil.)--Chinese University of Hong Kong, 2002.Includes bibliographical references (leaves 64-68).Abstracts in English and Chinese.Abstract --- p.iAcknowledgments --- p.viTable of Contents --- p.viiList of Figures --- p.ixList of Tables --- p.xChapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Automatic Personal Identification --- p.1Chapter 1.2 --- Biometrics --- p.2Chapter 1.2.1 --- Objectives --- p.2Chapter 1.2.2 --- Operational Mode --- p.3Chapter 1.2.3 --- Requirements --- p.3Chapter 1.2.4 --- Performance Evaluation --- p.4Chapter 1.2.5 --- Biometric Technologies --- p.4Chapter 1.3 --- Fingerprint --- p.6Chapter 1.3.1 --- Applications --- p.6Chapter 1.3.2 --- Advantages of Fingerprint Identification --- p.7Chapter 1.3.3 --- Permanence and Uniqueness --- p.8Chapter 1.4 --- Thesis Overview --- p.8Chapter 1.5 --- Summary --- p.9Chapter Chapter 2 --- Fingerprint Identification --- p.10Chapter 2.1 --- History of Fingerprints --- p.10Chapter 2.2 --- AFIS Architecture --- p.12Chapter 2.3 --- Fingerprint Acquisition --- p.15Chapter 2.4 --- Fingerprint Representation --- p.16Chapter 2.5 --- Fingerprint Classification --- p.18Chapter 2.6 --- Fingerprint Matching --- p.20Chapter 2.7 --- Challenges --- p.21Chapter 2.8 --- Combination Schemes --- p.22Chapter 2.9 --- Summary --- p.23Chapter Chapter 3 --- Live-Scan Fingerprint Database --- p.24Chapter 3.1 --- Live-Scan Fingerprint Sensors --- p.24Chapter 3.2 --- Database Features --- p.24Chapter 3.3 --- Filename Description --- p.28Chapter Chapter 4 --- Preprocessing for Skeleton-Based Minutiae Extraction --- p.30Chapter 4.1 --- Review of Minutiae-based Methods --- p.31Chapter 4.2 --- Skeleton-based Minutiae Extraction --- p.32Chapter 4.2.1 --- Preprocessing --- p.33Chapter 4.2.2 --- Validation of Bug Pixels and Minutiae Extraction --- p.40Chapter 4.3 --- Experimental Results --- p.42Chapter 4.4 --- Summary --- p.44Chapter Chapter 5 --- Post-Processing --- p.46Chapter 5.1 --- Review of Post-Processing Methods --- p.46Chapter 5.2 --- Post-Processing Algorithms --- p.47Chapter 5.2.1 --- H-Point --- p.47Chapter 5.2.2 --- Termination/Bifurcation Duality --- p.48Chapter 5.2.3 --- Post-Processing Procedure --- p.49Chapter 5.3 --- Experimental Results --- p.52Chapter 5.4 --- Summary --- p.54Chapter Chapter 6 --- Conclusions and Future Work --- p.58Chapter 6.1 --- Conclusions --- p.58Chapter 6.2 --- Problems and Future Works --- p.59Chapter 6.2.1 --- Problem 1 --- p.59Chapter 6.2.2 --- Problem 2 --- p.61Chapter 6.2.3 --- Problem 3 --- p.61Chapter 6.2.4 --- Future Works --- p.62Bibliography --- p.6

Fast fingerprint verification using sub-regions of fingerprint images.

Chan Ka Cheong.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 77-85).Abstracts in English and Chinese.Chapter 1. --- Introduction --- p.1Chapter 1.1 --- Introduction to Fingerprint Verification --- p.1Chapter 1.1.1 --- Biometrics --- p.1Chapter 1.1.2 --- Fingerprint History --- p.2Chapter 1.1.3 --- Fingerprint characteristics --- p.4Chapter 1.1.4 --- A Generic Fingerprint Matching System Architecture --- p.6Chapter 1.1.5 --- Fingerprint Verification and Identification --- p.8Chapter 1.1.7 --- Biometric metrics --- p.10Chapter 1.2 --- Embedded system --- p.12Chapter 1.2.1 --- Introduction to embedded systems --- p.12Chapter 1.2.2 --- Embedded systems characteristics --- p.12Chapter 1.2.3 --- Performance evaluation of a StrongARM processor --- p.13Chapter 1.3 --- Objective -An embedded fingerprint verification system --- p.16Chapter 1.4 --- Organization of the Thesis --- p.17Chapter 2 --- Literature Reviews --- p.18Chapter 2.1 --- Fingerprint matching overviews --- p.18Chapter 2.1.1 --- Minutiae-based fingerprint matching --- p.20Chapter 2.2 --- Fingerprint image enhancement --- p.21Chapter 2.3 --- Orientation field Computation --- p.22Chapter 2.4 --- Fingerprint Segmentation --- p.24Chapter 2.5 --- Singularity Detection --- p.25Chapter 2.6 --- Fingerprint Classification --- p.27Chapter 2.7 --- Minutia extraction --- p.30Chapter 2.7.1 --- Binarization and thinning --- p.30Chapter 2.7.2 --- Direct gray scale approach --- p.32Chapter 2.7.3 --- Comparison of the minutiae extraction approaches --- p.35Chapter 2.8 --- Minutiae matching --- p.37Chapter 2.8.1 --- Point matching --- p.37Chapter 2.8.2 --- Structural matching technique --- p.38Chapter 2.9 --- Summary --- p.40Chapter 3. --- Implementation --- p.41Chapter 3.1 --- Fast Fingerprint Matching System Overview --- p.41Chapter 3.1.1 --- Typical Fingerprint Matching System --- p.41Chapter 3.1.2. --- Fast Fingerprint Matching System Overview --- p.41Chapter 3.2 --- Orientation computation --- p.43Chapter 3.21 --- Orientation computation --- p.43Chapter 3.22 --- Smooth orientation field --- p.43Chapter 3.3 --- Fingerprint image segmentation --- p.45Chapter 3.4 --- Reference Point Extraction --- p.46Chapter 3.5 --- A Classification Scheme --- p.51Chapter 3.6 --- Finding A Small Fingerprint Matching Area --- p.54Chapter 3.7 --- Fingerprint Matching --- p.57Chapter 3.8 --- Minutiae extraction --- p.59Chapter 3.8.1 --- Ridge tracing --- p.59Chapter 3.8.2 --- cross sectioning --- p.60Chapter 3.8.3 --- local maximum determination --- p.61Chapter 3.8.4 --- Ridge tracing marking --- p.62Chapter 3.8.5 --- Ridge tracing stop criteria --- p.63Chapter 3.9 --- Optimization technique --- p.65Chapter 3.10 --- Summary --- p.66Chapter 4. --- Experimental results --- p.67Chapter 4.1 --- Experimental setup --- p.67Chapter 4.2 --- Fingerprint database --- p.67Chapter 4.3 --- Reference point accuracy --- p.67Chapter 4.4 --- Variable number of matching minutiae results --- p.68Chapter 4.5 --- Contribution of the verification prototype --- p.72Chapter 5. --- Conclusion and Future Research --- p.74Chapter 5.1 --- Conclusion --- p.74Chapter 5.2 --- Future Research --- p.74Bibliography --- p.7

Acceptable load carriage for primary school girls

Randomized six primary school girls aged between 9 to 10 years old completed this study at the Motion Analysis Laboratory, Department of Biomedical Engineering, University of Malaya, Malaysia. Three different loads were used (10, 15 and 20 of their body weight) and 0 was used as control during level walking. The data obtained, both kinetics and kinematics, were analyzed using the Peak Motus® 7.2.4 software from PEAK Performance Technologies® and SPSSO version 12.0 software. The results indicated that the peak ground reaction forces increased with increasing backpack loads. The hip and knee flexion/extension increased as the loads increased. The stride length and walking speed decreased, while the cadence showed no significant difference (P>0.05). If the trunk angle is taken as the criterion to determine acceptable backpack loads for children, these loads should not exceed 10 of the children's body weight. © EuroJournals Publishing, Inc. 2006