Advances in Biometrics for Secure Human Authentication System

In the recent decade, one of our major concerns in the global technological society of information security is confirmation that a person accessing confidential information is authorized to perform so. Such mode of access is generally accomplished by a person's confirming their identity by the use of some method of authentication system. In present days, the requirement for safe security in storing individual information has been developing rapidly and among the potential alternative is implementing innovative biometric identification techniques. This chapter discusses how the advent of the 20th century has brought forth the security principles of identification and authentication in the field of biometric analysis. The chapter reviews vulnerabilities in biometric authentication and issues in system implementation. The chapter also proposes the multifactor authentication and the use of multimodal biometrics, i.e., the combination of Electrocardiogram (ECG) and Phonocardiogram (PCG) signals to enhance reliability in the authentication process.

Veillance

This chapter builds upon the concept of Uberveillance introduced in the seminal research of M. G. Michael and Katina Michael in 2006. It begins with an overview of sousveillance (underwatching) technologies and examines the “We're watching you but you can't watch us” hypocrisy associated with the rise of surveillance (overwatching). Surveillance cameras are often installed in places that have “NO CAMERAS” and “NO CELLPHONES IN STORE, PLEASE!” signage. The author considers the chilling effect of this veillance hypocrisy on LifeGlogging, wearable computing, “Sixth Sense,” AR Glass, and the Digital Eye Glass vision aid. If surveillance gives rise to hypocrisy, then to what does its inverse, sousveillance (wearable cameras, AR Glass, etc.), give rise? The opposite (antonym) of hypocrisy is integrity. How might we resolve the conflict-of-interest that arises in situations where, for example, police surveillance cameras capture the only record of wrongdoing by the police? Is sousveillance the answer or will centralized dataveillance merely turn sousveillance into a corruptible uberveillance authority?

Hybrid Data Mining Approach for Image Segmentation Based Classification

Evolutionary harmony search algorithm is used for its capability in finding solution space both locally and globally. In contrast, Wavelet based feature selection, for its ability to provide localized frequency information about a function of a signal, makes it a promising one for efficient classification. Research in this direction states that wavelet based neural network may be trapped to fall in a local minima whereas fuzzy harmony search based algorithm effectively addresses that problem and able to get a near optimal solution. In this, a hybrid wavelet based radial basis function (RBF) neural network (WRBF) and feature subset harmony search based fuzzy discernibility classifier (HSFD) approaches are proposed as a data mining technique for image segmentation based classification. In this paper, the authors use Lena RGB image; Magnetic resonance image (MR) and Computed Tomography (CT) Image for analysis. It is observed from the obtained simulation results that Wavelet based RBF neural network outperforms the harmony search based fuzzy discernibility classifiers.

Dataveillance and Information Privacy Concerns

Information privacy research historically focuses on exploring individuals' concerns in the transaction environment. However, the recent growth of technology-enabled workplace surveillance is raising many concerns over employees' privacy. Employee surveillance practices are becoming increasingly prevalent, ranging from monitoring internet and email activities to capturing employees' interactions with customers and employees' personal health and fitness data using wearable health devices. Individuals may understand that employers can monitor their activities, but may not the potential uses or the repercussions of such monitoring. Moreover, employees may not feel they have the ability to opt-out of this monitoring. This chapter explores the privacy and ethical issues surrounding emerging means of workplace surveillance. The chapter considers both employee and employer perspectives and poses many questions to consider when deciding when does legitimate monitoring become an invasion of employee privacy?

Who “Screens” Security?

Films represent our awareness of surveillance and often trigger a deep emotional response from audiences, and for whole genres of film—particularly the political thriller and science fiction/speculative dystopia, along with horror films and some forms of the mystery or crime film—have been built around an individual or group of individuals who are being kept under some form of surveillance, either by the authorities of the state and by other individuals or groups who may have criminal and/or even psychotic motives. For filmmakers and their intended audiences, the surveillance narrative doubles back onto to very art form itself, composed as it is of the camera's surveillance of the action, along with the viewers' attentive watching of the film. While such audience attention had also been fundamental to drama for thousands of years, it has only been more recently that audiences began observing the fourth wall conventions of silence and darkness that make their watching of a performance a kind of surveillance.

A Survey and Surveillance Issues in Smart Homes Environment for Assistive Living

A surveillance system for assisting the elderly in remaining independent in their familiar environment is one of the subjects interest in recent healthcare studies. When mature, it is expected that this system will have the ability to track objects that a resident may lose periodically, detect falls within the home, alert family members or healthcare professionals to abnormal behaviors. This paper addresses the early stages and issues of the development of such a system, the physical characteristics of the system that is being designed, early results, and guidance on the future work that will have to be completed in the future.

Comparative Analysis of Efficient Platforms

With the advancement of technology we are heading towards a paperless environment. But there are still a large numbers of documents that exist in paper format in our daily lives. Thus the need to digitize these paper documents, archive them and view them at all times has arisen. The number of documents of a small organization may be in thousands, millions or even more. This chapter presents comparative analysis of different programming languages and libraries where it is intended to parallel process a huge stream of images which undergo unpredictable arrival of the images and variation in time. Since the parallelism can be implemented at different levels, different algorithms and techniques have also been discussed. It also presents the state of the art and discussion of various existing technical solutions to implement the parallelization on a hybrid platform for the real time processing of the images contained in a stream. Experimental results obtained using Apache Hadoop in combination with OpenMP have also been discussed.

A Hybrid Scheme for Breast Cancer Detection Using Intuitionistic Fuzzy Rough Set Technique

Diagnosis of cancer is of prime concern in recent years. Medical imaging is used to analyze these diseases. But, these images contain uncertainties due to various factors and thus intelligent techniques are essential to process these uncertainties. This paper hybridizes intuitionistic fuzzy set and rough set in combination with statistical feature extraction techniques. The hybrid scheme starts with image segmentation using intuitionistic fuzzy set to extract the zone of interest and then to enhance the edges surrounding it. Further feature extraction using gray-level co-occurrence matrix is presented. Additionally, rough set is used to engender all minimal reducts and rules. These rules then fed into a classifier to identify different zones of interest and to check whether these points contain decision class value as either cancer or not. The experimental analysis shows the overall accuracy of 98.3% and it is higher than the accuracy achieved by hybridizing fuzzy rough set model.

Study of Noise Removal Techniques for Digital Images

Images very often get contaminated by different types of noise like impulse noise, Gaussian noise, spackle noise etc. due to malfunctioning of camera sensors during acquisition or transmission using the channel. The noise in the channel affects processing of images in various ways. Hence, the image has to be restored by applying filtration process before the high level image processing. In general the restoration techniques for images are based up on the mathematical and the statistical models of image degradation. Denoising and deblurring are used to recover the image from degraded observations. The researchers have proposed verity of linear and non-linear filters for removal of noise from images. The filtering technique has been used to remove noisy pixels, without changing the uncorrupted pixel values. This chapter presents the metrics used for measurement of noise, and the various schemes for removing of noise from the images.

Color Features and Color Spaces Applications to the Automatic Image Annotation

Nowadays images play a crucial role in different fields such as medicine, advertisement, education and entertainment. Describing images content and retrieving them are important fields in image processing. Automatic image annotation is a process which produces words from a digital image based on the content of this the image by using a computer. In this chapter, after an introduction to neighbor voting algorithm for image annotation, we discuss the applicability of color features and color spaces in automatic image annotation. We discuss the applicability of three color features (color histogram, color moment and color Autocorrelogram) and three color spaces (RGB, HSI and YCbCr) in image annotation. Experimental results, using Corel5k benchmark annotated images dataset, demonstrate that using different color spaces and color features helps to select the best color features and spaces in image annotation area.