Prevention of Unauthorized Transport of Ore in Opencast Mines Using Automatic Number Plate Recognition

Security in mining is a primary concern, which mainly affects the production cost. An efficiently detecting and deterring theft will maximize the profitability of any mining organization. Many illegal transportation cases were registered in spite of rules imposed by central and state governments under Section 23 (c) of MMDR Act 1957. Use of an automated checkpoint gate based on license plate recognition and biometric fingerprint system for vehicle tracking enhances the security in mines. The method was tested on the number plates with various considerations like clean number plates, clean fingerprints, dusty and faded number plates, dusty fingerprints, and number plates captured by varying distance. By considering all the above conditions the pictures were processed by ANPR and bio-metric fingerprint modules. Vehicle license number plate was captured using a digital camera and the captured RGB image was converted to grayscale image. Thresholding was done to remove unwanted areas from the grayscale image. The characters of the number plate were segmented using Gabor filter. A track-sector matrix was generated by considering the number of pixels in each region and was matched with existing template to identify the character. The fingerprint scans the finger and matches with the template created at the time of fingerprint registration at the machine. The micro-controller accepted the processed output in binary form from ANPR and bio-metric fingerprint system. The micro-controller processed the binary output and the checkpoint gate was closed/open based on the output provided by the microcontroller to motor driver

Biometrics

Biometrics-Unique and Diverse Applications in Nature, Science, and Technology provides a unique sampling of the diverse ways in which biometrics is integrated into our lives and our technology. From time immemorial, we as humans have been intrigued by, perplexed by, and entertained by observing and analyzing ourselves and the natural world around us. Science and technology have evolved to a point where we can empirically record a measure of a biological or behavioral feature and use it for recognizing patterns, trends, and or discrete phenomena, such as individuals' and this is what biometrics is all about. Understanding some of the ways in which we use biometrics and for what specific purposes is what this book is all about

Applications of aerospace technology in the public sector

Current activities of the program to accelerate specific applications of space related technology in major public sector problem areas are summarized for the period 1 June 1971 through 30 November 1971. An overview of NASA technology, technology applications, and supporting activities are presented. Specific technology applications in biomedicine are reported including cancer detection, treatment and research; cardiovascular diseases, diagnosis, and treatment; medical instrumentation; kidney function disorders, treatment, and research; and rehabilitation medicine

Biometrics

Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book

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

Biometric Applications Based on Multiresolution Analysis Tools

This dissertation is dedicated to the development of new algorithms for biometric applications based on multiresolution analysis tools. Biometric is a unique, measurable characteristic of a human being that can be used to automatically recognize an individual or verify an individual\u27s identity. Biometrics can measure physiological, behavioral, physical and chemical characteristics of an individual. Physiological characteristics are based on measurements derived from direct measurement of a part of human body, such as, face, fingerprint, iris, retina etc. We focussed our investigations to fingerprint and face recognition since these two biometric modalities are used in conjunction to obtain reliable identification by various border security and law enforcement agencies. We developed an efficient and robust human face recognition algorithm for potential law enforcement applications. A generic fingerprint compression algorithm based on state of the art multiresolution analysis tool to speed up data archiving and recognition was also proposed. Finally, we put forth a new fingerprint matching algorithm by generating an efficient set of fingerprint features to minimize false matches and improve identification accuracy. Face recognition algorithms were proposed based on curvelet transform using kernel based principal component analysis and bidirectional two-dimensional principal component analysis and numerous experiments were performed using popular human face databases. Significant improvements in recognition accuracy were achieved and the proposed methods drastically outperformed conventional face recognition systems that employed linear one-dimensional principal component analysis. Compression schemes based on wave atoms decomposition were proposed and major improvements in peak signal to noise ratio were obtained in comparison to Federal Bureau of Investigation\u27s wavelet scalar quantization scheme. Improved performance was more pronounced and distinct at higher compression ratios. Finally, a fingerprint matching algorithm based on wave atoms decomposition, bidirectional two dimensional principal component analysis and extreme learning machine was proposed and noteworthy improvements in accuracy were realized

Surface Modeling and Analysis Using Range Images: Smoothing, Registration, Integration, and Segmentation

This dissertation presents a framework for 3D reconstruction and scene analysis, using a set of range images. The motivation for developing this framework came from the needs to reconstruct the surfaces of small mechanical parts in reverse engineering tasks, build a virtual environment of indoor and outdoor scenes, and understand 3D images. The input of the framework is a set of range images of an object or a scene captured by range scanners. The output is a triangulated surface that can be segmented into meaningful parts. A textured surface can be reconstructed if color images are provided. The framework consists of surface smoothing, registration, integration, and segmentation. Surface smoothing eliminates the noise present in raw measurements from range scanners. This research proposes area-decreasing flow that is theoretically identical to the mean curvature flow. Using area-decreasing flow, there is no need to estimate the curvature value and an optimal step size of the flow can be obtained. Crease edges and sharp corners are preserved by an adaptive scheme. Surface registration aligns measurements from different viewpoints in a common coordinate system. This research proposes a new surface representation scheme named point fingerprint. Surfaces are registered by finding corresponding point pairs in an overlapping region based on fingerprint comparison. Surface integration merges registered surface patches into a whole surface. This research employs an implicit surface-based integration technique. The proposed algorithm can generate watertight models by space carving or filling the holes based on volumetric interpolation. Textures from different views are integrated inside a volumetric grid. Surface segmentation is useful to decompose CAD models in reverse engineering tasks and help object recognition in a 3D scene. This research proposes a watershed-based surface mesh segmentation approach. The new algorithm accurately segments the plateaus by geodesic erosion using fast marching method. The performance of the framework is presented using both synthetic and real world data from different range scanners. The dissertation concludes by summarizing the development of the framework and then suggests future research topics

The use of serface fuintionalised silica nano-particlate powders for the identification of gunshot residues from fingerprints

Gunshot residue (GSR) mixture consists of partially burned particles of propellant and characteristic particles of elements originating from the primer, bullet, propellant and some additives in the propellant. Since Harrison and Gillory [1] drew forensic scientists’ attention to the fact that GSR contained trace amounts of inorganic compounds such as lead, barium and antimony, a number of analytical techniques have been tested trying to find and establish sensitive, selective and reliable methods to identify and analyse gunshot residues. The standard procedure for the analysis of gunshot residues involves imaging these small metallic particles using scanning electron microscopy (SEM) and subsequent compositional analysis using Energy Dispersive X-ray Analysis (EDX). This study focuses on the analysis organic compounds in GSR. It is motivated by the increasing need to overcome the problems with the analysis of lead-free ammunitions. A comprehensive literature review was performed in order to determine the most commonly encountered organic compounds in GSR. These compounds include diphenylamine, methylcentralite, ethylcentralite, nitroglycerine, 2-nitrodiphenylamine and 4-nitrodiphenylamine. It has been clearly demonstrated using standard materials and appropriate calibration curves that gas chromatograph and mass spectrometry (GC/MS) is capable of providing limits of detection that are consistent with the concentrations of the key organic constituents found in gunshot residues. Furthermore, we have demonstrated that the relative concentrations of seven key components can be used to provide branding information on the shotgun cartridges. A strong relationship was found between the chemical composition of fired and unfired powder. Therefore, it is possible to differentiate between two ammunition brands through the analysis of the organic constituents. Traditional fingerprint powders such as titanium dioxide, aluminium, carbon black, iron oxide, lycopodium spores and rosin are used to enhance fingerprint left at the scene of crime. More recently nanoparticles have been demonstrated to be highly effective for the enhancement of the fingerprints [2]. Silica nano-particulates of defined size and shape were synthesised and functionalised with two different functional groups (phenyl and long chain hydrocarbon) using a Tri- phasic Reverse Emulsion (TPRE) method. These nano-particulates were characterised using scan electron microscope (SEM), transmission electron microscopy (TEM), elemental analysis, particles size analyser, BET surface area and solid-state nuclear magnetic resonance (NMR) spectroscopy. These powders were used as an effective agent to visualise latent fingerprints on different surfaces. Furthermore, they have been utilised to absorb any organic materials within the fingerprint from the discharged of weapon. Analyses of the adsorbed organic residues were performed using GC/MS and Raman spectroscopy. The results showed that the synthesised silica nano-particulate fingerprint powder gave better result in term of their ability to absorb organic materials in GSR and enhance the visualisation of the latent fingerprint compared to a single commercial powder

Optical Methods in Sensing and Imaging for Medical and Biological Applications

The recent advances in optical sources and detectors have opened up new opportunities for sensing and imaging techniques which can be successfully used in biomedical and healthcare applications. This book, entitled ‘Optical Methods in Sensing and Imaging for Medical and Biological Applications’, focuses on various aspects of the research and development related to these areas. The book will be a valuable source of information presenting the recent advances in optical methods and novel techniques, as well as their applications in the fields of biomedicine and healthcare, to anyone interested in this subject