A schema for cryptographic keys generation using hybrid biometrics

Biometric identifiers refer to unique physical properties or behavioural attributes of individuals. Some of the well known biometric identifiers are voice, finger prints, retina or iris, facial structure etc. In our daily interaction with others directly or indirectly, we implicitly use biometrics to know, distinguish and trust people. Biometric identifiers represent the concept of "who a person is" by gathering vital characteristics that don't correspond to any other person. The human brain to some extent is able to ascertain disparities or variation in certain physical attributes and yet verify the authenticity of a person. But this is difficult to be implemented in electronic systems due to the intense requirements of artificial decision making and hard-coded logic. This paper examines the possibility of using a combination of biometric attributes to overcome common problems in having a single biometric scheme for authentication. It also investigates possible schemes and features to deal with variations in Biometric attributes. The material presented is related to ongoing research by the Computer Communications Research Group at Leeds Metropolitan University. We use this paper as a starting step and as a plan for advanced research. It offers ideas and proposition for implementing hybrid biometrics in conjunction with cryptography. This is work in progress and is in a very preliminary stage

Signature Verification Approach using Fusion of Hybrid Texture Features

In this paper, a writer-dependent signature verification method is proposed. Two different types of texture features, namely Wavelet and Local Quantized Patterns (LQP) features, are employed to extract two kinds of transform and statistical based information from signature images. For each writer two separate one-class support vector machines (SVMs) corresponding to each set of LQP and Wavelet features are trained to obtain two different authenticity scores for a given signature. Finally, a score level classifier fusion method is used to integrate the scores obtained from the two one-class SVMs to achieve the verification score. In the proposed method only genuine signatures are used to train the one-class SVMs. The proposed signature verification method has been tested using four different publicly available datasets and the results demonstrate the generality of the proposed method. The proposed system outperforms other existing systems in the literature.Comment: Neural Computing and Applicatio

Keystroke dynamics in the pre-touchscreen era

Biometric authentication seeks to measure an individual’s unique physiological attributes for the purpose of identity verification. Conventionally, this task has been realized via analyses of fingerprints or signature iris patterns. However, whilst such methods effectively offer a superior security protocol compared with password-based approaches for example, their substantial infrastructure costs, and intrusive nature, make them undesirable and indeed impractical for many scenarios. An alternative approach seeks to develop similarly robust screening protocols through analysis of typing patterns, formally known as keystroke dynamics. Here, keystroke analysis methodologies can utilize multiple variables, and a range of mathematical techniques, in order to extract individuals’ typing signatures. Such variables may include measurement of the period between key presses, and/or releases, or even key-strike pressures. Statistical methods, neural networks, and fuzzy logic have often formed the basis for quantitative analysis on the data gathered, typically from conventional computer keyboards. Extension to more recent technologies such as numerical keypads and touch-screen devices is in its infancy, but obviously important as such devices grow in popularity. Here, we review the state of knowledge pertaining to authentication via conventional keyboards with a view toward indicating how this platform of knowledge can be exploited and extended into the newly emergent type-based technological contexts