This thesis studies the applicability of the electrocardiogram signal (ECG) as a biometric. There is strong evidence that heart's electrical activity embeds highly distinctive characteristics, suitable for applications such as the recognition of human subjects. Such systems traditionally provide two modes of functionality, identification and authentication; frameworks for subject recognition are herein proposed and analyzed in both scenarios. As in most pattern recognition problems, the probability of mis-classification error decreases as more learning information becomes available. Thus, a central consideration is the design and evaluation of algorithms which exploit the added information provided by the 12 lead standard ECG recording system. Feature and decision level fusion techniques described in thesis, offer enhanced security levels. The main novelty of the proposed approach, lies in the design of an identification system robust to cardiac arrhythmias. Criteria concerning the power distribution and information theoretic complexity of electrocardiogram windows are defined to signify abnormal ECG recordings, not suitable for recognition. Experimental results indicate high recognition rates and highlight identification based on ECG signals as very promising.

This thesis studies the applicability of the electrocardiogram signal (ECG) as a biometric. There is strong evidence that heart's electrical activity embeds highly distinctive characteristics, suitable for applications such as the recognition of human subjects. Such systems traditionally provide two modes of functionality, identification and authentication; frameworks for subject recognition are herein proposed and analyzed in both scenarios. As in most pattern recognition problems, the probability of mis-classification error decreases as more learning information becomes available. Thus, a central consideration is the design and evaluation of algorithms which exploit the added information provided by the 12 lead standard ECG recording system. Feature and decision level fusion techniques described in thesis, offer enhanced security levels. The main novelty of the proposed approach, lies in the design of an identification system robust to cardiac arrhythmias. Criteria concerning the power distribution and information theoretic complexity of electrocardiogram windows are defined to signify abnormal ECG recordings, not suitable for recognition. Experimental results indicate high recognition rates and highlight identification based on ECG signals as very promising.

In a world dominated by smartphones, cloud computing, and online accounts, security of personal and corporate data is a critical concern. Voiceprint Vault provides a voice authentication service that can be used in a multitude of applications to secure sensitive data. Voiceprint Vault includes the following high-level features: - Cloud-based voice authentication using trusted signal processing algorithms - Multifactor authentication with use of optional password - Cross-platform compatibility using secure web requests to authenticate - Built-in storage and synching of private user data - Java library to facilitate integration with Android applications The Voiceprint Vault service allows users of an application to create an account, provide a voice sample, and then access the account with a simple spoken phrase. When users access their account, their voice sample is analyzed and compared to their training recordings. This system can be tailored to the needs of a particular user with per-user security options. It provides the convenience of voice access, but also allows for a password to be used for increased security. The Voiceprint Vault service is designed to allow application developers to integrate an existing, tested authentication system into their app rather than creating their own authentication system. The Voiceprint Vault server provides application specific repositories that developers can create to hold all user data, cryptographic information, and voice samples. The user data stored on the Voiceprint Vault server provides built-in synchronization across all connected devices. A reference implementation is provided that demonstrates the use of Voiceprint Vault authentication. The reference implementation is an Android app that uses the voice authentication service to protect access to personal notes, tasks, and dates that are synched across devices. Detailed instructions for integrating Voiceprint Vault into an existing application are also provided with the reference implementation. The accuracy of voiceprint authentication was investigated and optimized for a set of sample users and recordings. The security features and dangers of such a system are described along with recommendations for safe use. The optimal parameters to be used in the voice authentication algorithms are also presented in this report. / text

M. Tech. Computer System Engineering / A single biometric trait for authentication is widely used in some application areas where security is of high importance. However, biometric systems are susceptible to noise, intraclass variation, non-universality and spoof attacks. Thus, there is need to use algorithms that overcome all these limitations found in biometric systems. The use of multimodal biometrics can improve the performance of authentication system. This study proposed using both fingerprint and face for authentication in access system.

The identification of laboratory mice has been an important issue in pharmaceutical applications ever since tests have been performed on animals. As biometric identification has become an increasingly important issue over the past decade, attempts are underway to replace traditional identification methods, which are mostly invasive and limited in code space. This thesis discusses a project that aims at identifying mice by biometrically examining the blood vessel patterns in their ears. In the proposed algorithm, firstly, the blood vessel structure within the obtained images got enhanced before segmenting the image in blood vessel and non-blood vessel portions. In the next step a sufficient amount of unique feature points got extracted from the segmented image. The obtained feature points were afterwards used for the actual identification procedure. Out of 20 mice, 18 could be identified successfully using the proposed algorithm.

Thesis (M.S.)--West Virginia University, 2007. / Title from document title page. Document formatted into pages; contains viii, 42 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 39-40).

Thesis (M.S.)--West Virginia University, 2007. / Title from document title page. Document formatted into pages; contains x, 93 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 86-90).

Thesis (M.S.)--West Virginia University, 2007. / Title from document title page. Document formatted into pages; contains v, 53 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 51-53).

Thesis (Ph. D.)--Michigan State University. Dept. of Computer Science and Engineering, 2008. / Title from PDF t.p. (viewed on Mar. 30, 2009) Includes bibliographical references (p. 210-228). Also issued in print.

Thesis (M.S.)--West Virginia University, 2009. / Title from document title page. Document formatted into pages; contains x, 101 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 99-101).