Qualcomm Incorporated

Here Global B.v.

Amir Jalalirad

Fatih Murat Porikli

Daniel Hendricus Franciscus Dijkman

Bence Major

Catalin Capota

Moritz Neun

Marco Scavuzzo

Davide Belli

Michael Kopp

Michael Sprague

Certain aspects of the present disclosure provide techniques and apparatus for biometric authentication using an anti-spoofing protection model refined using online data. The method generally includes receiving a biometric data input for a user. Features for the received biometric data input are extracted through a first machine learning model. It is determined, using the extracted features for the received biometric data input and a second machine learning model, whether the received biometric data input for the user is authentic or inauthentic. It is determined whether to add the extracted features for the received biometric data input, labeled with an indication of whether the received biometric data input is authentic or inauthentic, to a finetuning data set. The second machine learning model is adjusted based on the finetuning data set.

Adaptive personalization for anti-spoofing protection in biometric authentication systems

Systems and methods are provided for training a machine learned model on a large number of devices, each device acquiring a local set of training data without sharing data sets across devices. The devices train the model on the respective device's set of training data. The devices communicate a parameter vector from the trained model asynchronously with a parameter server. The parameter server updates a master parameter vector and transmits the master parameter vector to the respective device.

Asynchronous parameter aggregation for machine learning

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Eindhoven, Netherlands

Amir Jalalirad