A human identification system based on Heart sounds and Gaussian Mixture Models

Zhao, Zhidong; Shen, Qinqin

doi:10.1109/bmei.2011.6098471

Cited by 4 publications

(1 citation statement)

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“…GMM has been widely used in voice recognition systems [Reynolds and Rose 1995]. This is why most of its uses related to biometric identification have focused on sounds emitted by the body, specifically the heart [Wahid et al 2012;Zhao and Shen 2011;Fatemian et al 2010]. Experiments achieved accuracy between 0.86 and 1.0 with medium-sized populations of subjects (between 10 and 80 subjects).…”

Section: Gaussian Mixture Model (Gmm)mentioning

confidence: 99%

A Survey of Wearable Biometric Recognition Systems

et al. 2016

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The growing popularity of wearable devices is leading to new ways to interact with the environment, with other smart devices, and with other people. Wearables equipped with an array of sensors are able to capture the owner's physiological and behavioural traits, and thus are well suited for biometric authentication to control other devices or access digital services. However, wearable biometrics have substantial differences from traditional biometrics for computer systems, such as fingerprints, eye features, or voice. In this paper we discuss these differences and analyse how researchers are approaching the wearable biometrics field. We review and provide a categorization of wearable sensors useful for capturing biometric signals. We analyse the computational cost of the different signal processing techniques, an important practical factor in constrained devices such as wearables. Finally, we review and classify the most recent proposals in the field of wearable biometrics in terms of the structure of the biometric system proposed, their experimental setup, and their results. We also present a critique of experimental issues such as evaluation and feasibility aspects, and offer some final thoughts on research directions that need attention in future work.

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Section: Gaussian Mixture Model (Gmm)mentioning

confidence: 99%