As the use of biometrics becomes more wide-spread, the privacy concerns that stem from the use of biometrics are becoming more apparent. As the usage of mobile devices grows, so does the desire to implement biometric identification into such devices. A large majority of mobile devices being used are mobile phones. While work is being done to implement different types of biometrics into mobile phones, such as photo based biometrics, voice is a more natural choice. The idea of voice as a biometric identifier has been around a long time. One of the major concerns with using voice as an identifier is the instability of voice. We have developed a protocol that addresses those instabilities and preserves privacy. This paper describes a novel protocol that allows a user to authenticate using voice on a mobile remote device without compromising their privacy. We first discuss the Vaulted Verification protocol, which has recently been introduced in research literature, and then describe its limitations. We then introduce a novel adaptation and extension of the Vaulted Verification protocol to voice, dubbed Vaulted Voice Verification ( V 3 ). Following that we show a performance evaluation and then conclude with a discussion of security and future work.
Due to biometric template characteristics that are susceptible to non-revocable and privacy invasion, cancellable biometrics has been introduced to tackle these issues. In this paper, we present a two-factor cancellable formulation for speech biometrics, which we refer as probabilistic random projection (PRP). PRP offers strong protection on speech template by hiding the actual speech feature through the random subspace projection process. Besides, the speech template is replaceable and can be reissued when it is compromised. Our proposed method enables the generation of different speech templates from the same speech feature, which means linkability is not exited between the speech templates. The formulation of the cancellable biometrics retains its performance as for the conventional biometric. Besides that, we also propose 2D subspace projection techniques for speech feature extraction, namely 2D Principle Component Analysis (2DPCA) and 2D CLAss-Featuring Information Compression (2DCLAFIC) to accommodate the requirements of PRP formulation.