Biometric Home Security Recognition and Authentication (Term Paper Sample)

Biometric Home Security Recognition and Authentication
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Table of Contents TOC \o "1-3" \h \z \u Drafted Project Report PAGEREF _Toc73038317 \h 3Problem description PAGEREF _Toc73038318 \h 3Analysis of likely impact PAGEREF _Toc73038319 \h 3Proposed solution PAGEREF _Toc73038320 \h 3Aims and Objectives PAGEREF _Toc73038321 \h 4Research Questions PAGEREF _Toc73038322 \h 4Literature review PAGEREF _Toc73038323 \h 6Face Recognition PAGEREF _Toc73038324 \h 7Voice recognition PAGEREF _Toc73038325 \h 8Hand Geometry Recognition PAGEREF _Toc73038326 \h 8Smart home system PAGEREF _Toc73038327 \h 11Project work and its outcome PAGEREF _Toc73038328 \h 12Review PAGEREF _Toc73038329 \h 13Review of current stage of project work PAGEREF _Toc73038330 \h 13Review project management PAGEREF _Toc73038331 \h 17Review project management PAGEREF _Toc73038332 \h 18Method PAGEREF _Toc73038333 \h 18Lifecycle model PAGEREF _Toc73038334 \h 19Assessment of the risks to project completion PAGEREF _Toc73038335 \h 19Skills and Resources PAGEREF _Toc73038336 \h 19Review of personal development PAGEREF _Toc73038337 \h 21Reflection PAGEREF _Toc73038338 \h 21References PAGEREF _Toc73038339 \h 22Appendix A PAGEREF _Toc73038340 \h 24Appendix B PAGEREF _Toc73038341 \h 25Appendix C PAGEREF _Toc73038342 \h 28Work Breakdown Schedule PAGEREF _Toc73038343 \h 28
Drafted Project Report
Biometric Home Security Recognition and Authentication
Problem description
Consumers benefit from the advancement of information and communication technology (ICT), which simplifies and facilitates various aspects of human life. Home security is a priority for many households; as a result of homes being left unattended for the majority of the day, home invasion crimes are at an all-time high due to the difficulty of constantly monitoring the home (Chitnis et al., 2016)
Analysis of likely impact
Smart home technology such as smart locks, smart cameras, and smart video doorbells may be utilized to create a home security system. Utilizing biometrics adds a layer of security to a consumer's security system, allowing them to protect their home or valuables. Biometric data is unique to each person and is used to identify their identification by analyzing their physical traits (such as fingerprints, iris, or veins) (Lourde and Khosla, 2010). Cloning biometric data is difficult, which is why biometrics are ideal for security systems, in contrast to knowledge-based approaches (password or PIN) that may be forgotten or hacked (Lourde and Khosla, 2010) and token-based systems (such as an identification card) that may be lost or stolen (Lourde and Khosla, 2010).
Proposed solution
After reflecting on my current solution, I have proposed a revised one for the project; the project will continue to include research into the Internet of Things devices and biometrics, including home security; this will build on my prior OU study of modules T215, T216, and TM357; also, I have investigated how a biometric template is captured and stored.
Aims and Objectives
The goal of this study was to look at various solutions for a biometric security system that would enable authorized members of my family to complete access to the property while also protecting it against illegal entry or theft. The following are the project's questions, as well as the research's goals and objectives.
Research Questions
* What is the problem?
My present home security system requires me to input a PIN on an electronic pad to obtain entry. The issue is that this strategy is insecure due to the ease with which PINs may be guessed.
* Why is this a problem?
The electronic pad's PIN technique is insecure; passwords are also forgettable and maybe guessed or cracked by network hackers or malicious users (Ansari and Qutbuddin, 2009). Numerous studies have shown that numerical codes, such as a PIN on a keypad, as well as smart cards and physical keys, all have their disadvantages (Ishengoma, 2014).
* What will be the benefits of solving this issue?
The primary benefits of resolving this issue are as follows: first, the end-user will no longer need to remember a password or PIN to gain access to the property because their biometric data (for example, fingerprint, iris, etc.) is unique to them and can be used as a key to allow the authorized user access and keep out unauthorized users. Finally, security will be the primary benefit, as the new system will keep unauthorized individuals out of the property. After all, access will require a perfect match of biometric data.
* What are the Key aspects of ICT?
The key ICT aspects of this project are as follows
* The gathering or collection of biometric data using a reader or scanning device to capture the biometric data.
* Computer software will be used so that the captured biometric data can be converted into a format that a computer or cloud-based system can use, the data, which can then be stored on a computer system or database for retrieval, match points captured from the biometric image can be used to authenticate the individual.
* The user will use their biometrics to gain access to the property in the case of fingerprint authentication the user will place their finger on the reader/scanner. When the submitted biometrics has been checked against the biometric data that is stored in the system and a positive match is found then access will be permitted otherwise access will be denied. A message will be sent to the system admin either by email or a text message on their phone informing them that an unauthorized user has attempted to access the property.
* The encryption of user data and what algorithms are used to encrypt the data.
* What is my existing knowledge?
I have expanded my previous studies of TM357 where I studied IoT (Internet of Things) devices and IoT security, I have also used my knowledge of smart home technologies which I have implemented into my own home.
* Project output?
My project output is in the form of a research project, I have investigated the physical characteristics surrounding biometric data, I have also expanded my knowledge on the capture and storage of biometric data, how is the data stored, is the data classed as personal data. I have also investigated any social, ethical, and legal issues surround the use of biometric data. Moreover, I have also explored any security issues such as system security for example the encryption of user data.
Literature review
The system validates a person's identification in the verification mode by relating the acquired biometric characteristics to the person's biometric template recorded in the system database. A person wishing to be definitively identified asserts a factor in this system, most often through a personal identification number (PIN) or a user name, and the framework system does a one-to-one matching to decide if the statement is authentic or false. This style of inquiry is used to determine if a person is who she or he purports to be.
The individual's identity is determined using this approach by scanning the database's template for a match. As a result, a system performs one-to-many evaluations to ascertain a person's identification. The identification mode categorizes and identifies a previously unidentified identity. Its purpose is to provide answers to inquiries for example "who is this individual?" Distinctive evidence is crucial in negative affirmation applications because it demonstrates if the individual is who she claims to be. The purpose of negative acknowledgment is to dissuade a person from assuming several identities. Identification may also be used in conjunction with positive acknowledgment for convenience's sake. While conventional techniques of personal identification such as tokens and passwords are effective for identification, only biometrics are effective for negative identification.
Face Recognition
Bledsoe in 1966, suggested a face recognition system based on a few criteria such as ratios between feature points and normalized distances. Moreover, Nagao, Kanade, and Toshiyuki developed basic anthropometric and heuristic algorithms for face identification in 1979. These algorithms extract feature points on the human face such as mouth, eyes, and noise.
Matthew, Alex, and Turk proposed a Face recognition system based on Eigen-faces in 1991. Where a framework for human face detection and identification is provided. The algorithm was able to distinguish faces automatically in this work without any direction.
Kaneko, Hori, and Takeshi presented Joint Haar-like Face Detection Features in 2005. In this experimentation, the detector outperforms Viola and Jones's detector in terms of classification performance and error reduction by thirty-seven percent. Additionally, the detector is 2.6 times more sensitive.
Aaima, Zeeshan, and Faizan performed experimentation on Image-based Face Recognition and Detection in 2012 to assess face identification and detection approaches and to provide a comprehensive solution for image-based face recognition and detection.
Recent research presented a unique approach for extracting face features using Gabor Wavelets, PCA, and SVM. Although Gabor representations were heavily employed, our technique incorporates the individuality of Gabor characteristics. They said that the suggested approach is the most effective approach for dealing with light, poses, and expression.
Voice recognition
Since the 1930s, Bell Labo...