Qualcomm Incorporated

Bence Major

Amin Ansari

Ravi Teja Sukhavasi

Daniel Hendricus Franciscus Fontijne

Sundar Subramanian

Xinzhou Wu

Radhika Dilip Gowaikar

Daniel Hendricus Franciscus Dijkman

Fatih Murat Porikli

Hasib Siddiqui

Nathan Felix Altman

Michael John Hamilton

Javier Frydman

Nojun Kwak

Amir Jalalirad

Shubhankar Mange Borse

Teck Yian Lim

Adi Hendel

Davide Belli

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

Aspects described herein provide a method of performing guided training of a neural network model, including: receiving supplementary domain feature data; providing the supplementary domain feature data to a fully connected layer of a neural network model; receiving from the fully connected layer supplementary domain feature scaling data; providing the supplementary domain feature scaling data to an activation function; receiving from the activation function supplementary domain feature weight data; receiving a set of feature maps from a first convolution layer of the neural network model; fusing the supplementary domain feature weight data with the set of feature maps to form fused feature maps; and providing the fused feature maps to a second convolution layer of the neural network model.

Guided training of machine learning models with convolution layer feature data fusion

Some disclosed methods involve obtaining current A-line data corresponding to reflections of ultrasonic waves from a target object detected by a single receiver pixel, obtaining current ultrasonic fingerprint image data corresponding to reflections of ultrasonic waves from a target object surface, obtaining previously-obtained A-line data that was previously obtained from an authorized user, and obtaining previously-obtained ultrasonic fingerprint image data that was previously obtained from the authorized user. Some disclosed methods involve estimating, based at least in part on the current A-line data, the previously-obtained A-line data, the current ultrasonic fingerprint image data and the previously-obtained ultrasonic fingerprint image data, whether the target object is a finger of the authorized user. The estimation may involve an anti-spoofing process based at least in part on the current A-line data and the previously-obtained A-line data.

Ultrasonic fingerprint anti-spoofing based on A-line data

Disclosed are techniques for employing deep learning to analyze radar signals. In an aspect, an on-board computer of a host vehicle receives, from a radar sensor of the vehicle, a plurality of radar frames, executes a neural network on a subset of the plurality of radar frames, and detects one or more objects in the subset of the plurality of radar frames based on execution of the neural network on the subset of the plurality of radar frames. Further, techniques for transforming polar coordinates to Cartesian coordinates in a neural network are disclosed. In an aspect, a neural network receives a plurality of radar frames in polar coordinate space, a polar-to-Cartesian transformation layer of the neural network transforms the plurality of radar frames to Cartesian coordinate space, and the neural network outputs the plurality of radar frames in the Cartesian coordinate space.

Radar deep learning

Disclosed are techniques for fusing camera and radar frames to perform object detection in one or more spatial domains. In an aspect, an on-board computer of a host vehicle receives, from a camera sensor of the host vehicle, a plurality of camera frames, receives, from a radar sensor of the host vehicle, a plurality of radar frames, performs a camera feature extraction process on a first camera frame of the plurality of camera frames to generate a first camera feature map, performs a radar feature extraction process on a first radar frame of the plurality of radar frames to generate a first radar feature map, converts the first camera feature map and/or the first radar feature map to a common spatial domain, and concatenates the first radar feature map and the first camera feature map to generate a first concatenated feature map in the common spatial domain.

Early fusion of camera and radar frames

Methods of processing vehicle sensor information for object detection may include capturing generating a feature map based on captured sensor information, associating with each pixel of the feature map a prior box having a set of two or more width priors and a set of two or more height priors, determining a confidence value of each height prior and each width prior, outputting an indication of a detected object based on a highest confidence height prior and a highest confidence width prior, and performing a vehicle operation based on the output indication of a detected object. Embodiments may include determining for each pixel of the feature map one or more prior boxes having a center value, a size value, and a set of orientation priors, determining a confidence value for each orientation prior, and outputting an indication of the orientation of a detected object based on the highest confidence orientation.

Processing sensor information for object detection

Growing community of inventors

Amsterdam, Netherlands

Bence Major