Iris recognition using gabor filter / Zakhirulnizam Arshad

A biometric system provides automatic identification of a person based on a unique feature or characteristic possessed by the individual. Iris recognition is regarded as the most reliable and accurate biometric identification system available. The iris recognition prototype process was started with an enrollment process where eye image will be process by performing automatic segmentation system that is based on the Hough transform. The segmentation process produced the extracted iris region from an eye and then normalized into a rectangular block with constant dimensions to account for imaging inconsistencies. Finally, the phase data from 1D Log-Gabor filters was extracted and quantized to four levels to encode the unique pattern of the iris into a bit-wise biometric template and save it with require information. For identification process, eye image once again will be employed and process. The Hamming distance function was used for to find the matching between the two iris templates, and information of person will be displayed if both them found to match. Functionality testing shows that every functions in the system work and running well in enrollment process and also identification process. The result of accuracy test using 30 images show the matching rate of 57% of true match and 40% of false match. There are few limitations that can be improved for the future such as using hybrid Gabor Filter with any available feature extraction technique to eliminate noise and enhance the image. The prototype also can be improving by integrate it with the use of infra-red imaging device to capture the eye images in real life

Electronic word of mouth in social media: The common characteristics of retweeted and favourited marketer-generated content posted on Twitter

Marketers desire to utilise electronic word of mouth (eWOM) marketing on social media sites. However, not all online content generated by marketers has the same effect on consumers; some of them are effective while others are not. This paper aims to examine different characteristics of marketer-generated content (MGC) that of which one lead users to eWOM. Twitter was chosen as one of the leading social media sites and a content analysis approach was employed to identify the common characteristics of retweeted and favourited tweets. 2,780 tweets from six companies (Booking, Hostelworld, Hotels, Lastminute, Laterooms and Priceline) operating in the tourism sector are analysed. Results indicate that the posts which contain pictures, hyperlinks, product or service information, direct answers to customers and brand centrality are more likely to be retweeted and favourited by users. The findings present the main eWOM drivers for MGC in social media.Abdulaziz Elwalda and Mohammed Alsagga

Innovation in Private Infrastructure Development Effects of the Selection Environment and Modularity

This study investigates how the selection environment and modularity affect innovation in private infrastructure development. Our findings stem from an in-depth empirical study of the extent ten process innovations were implemented in an airport expansion programme. Our findings suggest that developer and customers can each occasionally champion or resist innovations. An innovation succeeds contingent upon the capability of the stakeholder groups to develop collectively a plan to finance and implement the innovation, which reconciles subjective individual assessments. Innovations can be particularly hard to adopt when they require financing from different budgets, or when the developer’s investment pays off only if customers behave in a specified way in the future. We also find that the degrees of novelty and modularity neither represent sufficient or necessary conditions enabling or hindering innovation. Novelty, however, makes the innovation champion’s job harder because it leads to perceptions of downside risk and regulatory changes, whereas modularity helps the champion operationalise ways that moderate resistance to innovate.Innovation; financing; implementation

Envisioning technology through discourse: a case study of biometrics in the National Identity Scheme in the United Kingdom

Around the globe, governments are pursuing policies that depend on information technology (IT). The United Kingdom’s National Identity Scheme was a government proposal for a national identity system, based on biometrics. These proposals for biometrics provide us with an opportunity to explore the diverse and shifting discourses that accompany the attempted diffusion of a controversial IT innovation. This thesis offers a longitudinal case study of these visionary discourses. I begin with a critical review of the literature on biometrics, drawing attention to the lack of in-depth studies that explore the discursive and organizational dynamics accompanying their implementation on a national scale. I then devise a theoretical framework to study these speculative and future-directed discourses based on concepts and ideas from organizing visions theory, the sociology of expectations, and critical approaches to studying the public’s understanding of technology. A methodological discussion ensues in which I explain my research approach and methods for data collection and analysis, including techniques for critical discourse analysis. After briefly introducing the case study, I proceed to the two-part analysis. First is an analysis of government actors’ discourses on biometrics, revolving around formal policy communications; second is an analysis of media discourses and parliamentary debates around certain critical moments for biometrics in the Scheme. The analysis reveals how the uncertain concept of biometrics provided a strategic rhetorical device whereby government spokespeople were able to offer a flexible yet incomplete vision for the technology. I contend that, despite being distinctive and offering some practical value to the proposals for national identity cards, the government’s discourses on biometrics remained insufficiently intelligible, uninformative, and implausible. The concluding discussion explains the unraveling visions for biometrics in the case, offers a theoretical contribution based on the case analysis, and provides insights about discourses on the ‘publics’ of new technology such as biometrics

HBSI Automation Using the Kinect 2

This research focused on classifying Human-Biometric Sensor Interaction errors in real-time. The Kinect 2 was used as a measuring device to track the position and movements of the subject through a simulated border control environment. Knowing, in detail, the state of the subject ensures that the human element of the HBSI model is analyzed accurately. A network connection was established with the iris device to know the state of the sensor and biometric system elements of the model. Information such as detection rate, extraction rate, quality, capture type, and more metrics was available for use in classifying HBSI errors. A Federal Inspection Station (FIS) booth was constructed to simulate a U.S. border control setting in an International airport. The subjects were taken through the process of capturing iris and fingerprint samples in an immigration setting. If errors occurred, the Kinect 2 program would classify the error and saved these for further analysis

Behaviour Profiling for Mobile Devices

With more than 5 billion users globally, mobile devices have become ubiquitous in our daily life. The modern mobile handheld device is capable of providing many multimedia services through a wide range of applications over multiple networks as well as on the handheld device itself. These services are predominantly driven by data, which is increasingly associated with sensitive information. Such a trend raises the security requirement for reliable and robust verification techniques of users.This thesis explores the end-user verification requirements of mobile devices and proposes a novel Behaviour Profiling security framework for mobile devices. The research starts with a critical review of existing mobile technologies, security threats and mechanisms, and highlights a broad range of weaknesses. Therefore, attention is given to biometric verification techniques which have the ability to offer better security. Despite a large number of biometric works carried out in the area of transparent authentication systems (TAS) and Intrusion Detection Systems (IDS), each have a set of weaknesses that fail to provide a comprehensive solution. They are either reliant upon a specific behaviour to enable the system to function or only capable of providing security for network based services. To this end, the behaviour profiling technique is identified as a potential candidate to provide high level security from both authentication and IDS aspects, operating in a continuous and transparent manner within the mobile host environment.This research examines the feasibility of a behaviour profiling technique through mobile users general applications usage, telephone, text message and multi-instance application usage with the best experimental results Equal Error Rates (EER) of 13.5%, 5.4%, 2.2% and 10% respectively. Based upon this information, a novel architecture of Behaviour Profiling on mobile devices is proposed. The framework is able to provide a robust, continuous and non-intrusive verification mechanism in standalone, TAS or IDS modes, regardless of device hardware configuration. The framework is able to utilise user behaviour to continuously evaluate the system security status of the device. With a high system security level, users are granted with instant access to sensitive services and data, while with lower system security levels, users are required to reassure their identity before accessing sensitive services.The core functions of the novel framework are validated through the implementation of a simulation system. A series of security scenarios are designed to demonstrate the effectiveness of the novel framework to verify legitimate and imposter activities. By employing the smoothing function of three applications, verification time of 3 minutes and a time period of 60 minutes of the degradation function, the Behaviour Profiling framework achieved the best performance with False Rejection Rate (FRR) rates of 7.57%, 77% and 11.24% for the normal, protected and overall applications respectively and with False Acceptance Rate (FAR) rates of 3.42%, 15.29% and 4.09% for their counterparts

CHARACTERIZING HABITUATION USING THE TIME-ON-TASK METRIC IN AN IRIS RECOGNITION SYSTEM

This thesis presents a characterization of biometric habituation in an iris recognition study using qualitative analysis of a distributed habituation survey and quantitative analysis of iris images collected in 2010 and 2012. The performed analyses answered the following two questions: a) How consistently does the biometric community define habituation?; and b) Does the time-on-task variable provide enough evidence to indicate the existence of habituation in an iris recognition system? The qualitative analysis examined responses to 12 habituation-related questions from 13 biometric experts to identify common themes that not only determined definition consistency but also characterized critical components often omitted from habituation definitions. Upon completion of the survey analysis, this study concluded that while aspects of habituation were universally understood, habituation in its entirety was not. The quantitative analysis examined trends in mean time-on-task using number of visits as a covariate. Subjects repeatedly (20 captures per visit and 25 maximum attempts per visit) interacted with an iris recognition camera, returning for at least eight visits. The trends in the resulting time-on-task, image quality and matching performance indicated that habituation effects were identifiable near the end of the 2012 collection

Archaeology of the Moving Image (Volume 1, Summer 2022)

A compilation of postgraduate student research projects written between 2017 and 2021 for a module titled Archaeology of the Moving Image in the Department of Media, Communications and Cultural Studies at Goldsmiths, University of London. Archaeology of the Moving Image is a course that encourages students to undertake independent investigations of the relationship between the past, present and future of moving image culture

Recognition of Nonideal Iris Images Using Shape Guided Approach and Game Theory

Most state-of-the-art iris recognition algorithms claim to perform with a very high recognition accuracy in a strictly controlled environment. However, their recognition accuracies significantly decrease when the acquired images are affected by different noise factors including motion blur, camera diffusion, head movement, gaze direction, camera angle, reflections, contrast, luminosity, eyelid and eyelash occlusions, and problems due to contraction and dilation. The main objective of this thesis is to develop a nonideal iris recognition system by using active contour methods, Genetic Algorithms (GAs), shape guided model, Adaptive Asymmetrical Support Vector Machines (AASVMs) and Game Theory (GT). In this thesis, the proposed iris recognition method is divided into two phases: (1) cooperative iris recognition, and (2) noncooperative iris recognition. While most state-of-the-art iris recognition algorithms have focused on the preprocessing of iris images, recently, important new directions have been identified in iris biometrics research. These include optimal feature selection and iris pattern classification. In the first phase, we propose an iris recognition scheme based on GAs and asymmetrical SVMs. Instead of using the whole iris region, we elicit the iris information between the collarette and the pupil boundary to suppress the effects of eyelid and eyelash occlusions and to minimize the matching error. In the second phase, we process the nonideal iris images that are captured in unconstrained situations and those affected by several nonideal factors. The proposed noncooperative iris recognition method is further divided into three approaches. In the first approach of the second phase, we apply active contour-based curve evolution approaches to segment the inner/outer boundaries accurately from the nonideal iris images. The proposed active contour-based approaches show a reasonable performance when the iris/sclera boundary is separated by a blurred boundary. In the second approach, we describe a new iris segmentation scheme using GT to elicit iris/pupil boundary from a nonideal iris image. We apply a parallel game-theoretic decision making procedure by modifying Chakraborty and Duncan's algorithm to form a unified approach, which is robust to noise and poor localization and less affected by weak iris/sclera boundary. Finally, to further improve the segmentation performance, we propose a variational model to localize the iris region belonging to the given shape space using active contour method, a geometric shape prior and the Mumford-Shah functional. The verification and identification performance of the proposed scheme is validated using four challenging nonideal iris datasets, namely, the ICE 2005, the UBIRIS Version 1, the CASIA Version 3 Interval, and the WVU Nonideal, plus the non-homogeneous combined dataset. We have conducted several sets of experiments and finally, the proposed approach has achieved a Genuine Accept Rate (GAR) of 97.34% on the combined dataset at the fixed False Accept Rate (FAR) of 0.001% with an Equal Error Rate (EER) of 0.81%. The highest Correct Recognition Rate (CRR) obtained by the proposed iris recognition system is 97.39%