ACCURATELY
IDENTIFYING PERSONS
OF INTEREST....
INNOVATIVE
One huge advantage is that the model uses both physiological and anatomical characterization of the heart, unlike other methods that mainly use only physiological characterization of the heart. By combining features from different leads, the heart of the person is better characterized in terms of anatomical orientation because each lead represents a different projection of the electrical vector of the heart. Thus, employing multiple electrocardiographic leads provides a better performance in subject verification or identification.
SECURE
The advantage of using cardiac biometrics over existing methods is that heart signatures are more difficult to forge compared to other biometric devices. Iris scanners can be fooled by contact lenses or sunglasses, and a segment of the population does not have readable fingerprints due to age or working conditions.
ACCURATE
Previous electrocardiographic signals employed a single template and compared that template with new test templates by means of cross-correlation or linear-discriminant analysis.The benefit of this technology over competing cardiac biometric methods is that it is more reliable with a significant reduction in error rates.
OUR TECHNOLOGY
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OUR STORY
Co-Founder Joseph "Joe" Wesley, M.B.A. is a former NFL Player with 16 years of experience as a Safety Professional in the oil and gas, chemicals, construction and manufacturing industries. Co-Founder Erica Morgan West is a physicist with 15 years of research and development experience in space physics and advanced materials.
APPLICATIONS
This technology has several potential applications:
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Personnel identity verification
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Mobile biometrics
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Justice/Law enforcement
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Homeland Security/Airports/ National ID documents
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Military Organizations
OUR TECHNOLOGY
The benefit of this technology is that it creates a probabilistic model of the electrocardiographic features of a person instead of a single signal template of the average heartbeat. The probabilistic model described as Gaussian mixture model allows various modes of the feature distribution, in contrast to a template model (widely used by traditional biometrics) that only characterizes a mean waveform.
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Tel: (615) 473-3268
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San Jose, CA 95136