Person identification and imposter detection using footfall generated seismic signals

A smart device for person identification and imposter detection, wherein the smart device comprises:

• at least one sensing module to detect a plurality of seismic signals generated from behavioral properties of a person, wherein the behavioral properties of the person comprise at least a footfall signature of the person, the footfall signature associated to corresponding footfalls of the person to generate a seismic event;
• an analog-to-digital converter module to convert the detected plurality of seismic signals, the plurality of seismic signals being analog signals, into digital signals:
• an event extraction module to:
• split each digital seismic signal, of plurality of digital seismic signals, into N equal segments;
• extract, for the each digital signal of the plurality of digital signals, first feature vectors (FE-I) corresponding to time domain and frequency domain features from each segment of the N equal segments;
• cluster, each of the first feature vectors (FE-I) between clustered events without using predefined thresholds, using a Gaussian Mixture Model (GMM), wherein one cluster represents footfall events, and another cluster represents noise events, to form at least one trained GMM based model capable of distinguishing the footfall events from the noise events based on one or more statistical properties;
• multiply the each segment of the N equal segments classified as the footfall events with a Gaussian window centered at segment's peak amplitude to obtain smooth and spectrally consistent footfall signals;
• extract, from the each resulting smooth and spectrally consistent footfall signal, second feature vectors (FE-II), corresponding to refined time domain and frequency domain features, to further train:
• a plurality of one-class learning models, each trained with the second feature vectors (FE-II) of an individual registered user to enable imposter detection by modelling only user specific class;
• and
• a multi-class learning model using the second feature vectors (FE-II) to associate footfall signatures with corresponding registered users;
• and
• store the trained GMM based model;
• and
• an identification module to detect an imposter and/or to identify a registered user during a live phase comprising user activity, wherein the imposter is detected if each of second feature vectors (FE-II) obtained during the user activity are identified as outliers by the all trained plurality of one-class learning models, and wherein if the each of the second feature vectors (FE-II) obtained during the user activity are not identified as outliers by any of the all trained plurality of one-class learning models, the registered user is identified by the multi-class learning model, wherein the each of the second feature vectors (FE-II) are classified into predefined user class corresponding to the registered user.

A biometric authentication system for person identification and imposter detection, the biometric authentication system comprising:

• an array of smart devices distributed over a pre-determined zone;
• and
• a central controller communicably coupled to the array of smart devices, wherein each smart device of the array of smart devices comprises:
• at least one sensing module to detect a plurality of seismic signals generated from behavioral properties of a person, wherein the behavioral properties of the person comprise at least a footfall signature of the person, the footfall signature associated to corresponding footfalls of the person to generate a seismic event;
• an analog-to-digital converter module to convert the detected plurality of seismic signals, the plurality of seismic signals being analog signals, into digital signals;
• an event extraction module to:
• split each digital seismic signal, of plurality of digital seismic signals, into N equal segments;
• extract, for the each digital signal of the plurality of digital signals, first feature vectors (FE-I) corresponding to time domain and frequency domain features from each segment of the N equal segments;
• cluster, each of the first feature vectors (FE-I) between clustered events without using predefined thresholds, using a Gaussian Mixture Model (GMM), wherein one cluster represents footfall events, and another cluster represents noise events, to form at least one trained GMM based model capable of distinguishing the footfall events from the noise events based on one or more statistical properties;
• multiply the each segment of the N equal segments classified as the footfall events with a Gaussian window centered at segment's peak amplitude to obtain smooth and spectrally consistent footfall signals;
• extract, from the each resulting smooth and spectrally consistent footfall signal, second feature vectors (FE-II), corresponding to refined time domain and frequency domain features, to further train:
• a plurality of one-class learning models, each trained with the second feature vectors (FE-II) of an individual registered user to enable imposter detection by modelling only user specific class;
• and
• a multi-class learning model using the second feature vectors (FE-II) to associate footfall signatures with corresponding registered users;
• and
• store the trained GMM based model;
• and
• an identification module to detect an imposter and/or to identify a registered user during a live phase comprising user activity, wherein the imposter is detected if each of second feature vectors (FE-II) obtained during the user activity are identified as outliers by the all trained plurality of one-class learning models, and wherein if the each of the second feature vectors (FE-II) obtained during the user activity are not identified as outliers by any of the all trained plurality of one-class learning models, the registered user is identified by the multi-class learning model, wherein the each of the second feature vectors (FE-II) are classified into predefined user class corresponding to the registered user.