Motorola Solutions, Inc.

Patrick Henson

Pietro Russo

Dharanish Kedarisetti

Aral Sarrafi

Teresa Dankel

Systems for detecting liveness in image data may perform a process including receiving, from one or more image capture devices, two-dimensional image data representing two images captured simultaneously and depicting the same human person, including data representing light captured in two spectral bands, such as visible light and infrared light. The received image data may be converted to grayscale or downsampled prior to further processing. The process may include creating a two-dimensional combined image representation of the received image data and analyzing the combined image representation to detect any implied three-dimensional features using a neural network or machine learning. The process may include classifying the received image data as likely to depict a live person or a two-dimensional spoof of a live person, dependent on the analysis, and outputting a classification result indicating a likelihood that the received image data depicts a live person.

Systems and methods for detecting liveness in captured image data

A method of detecting anomalous poses is provided, including: detecting an object of interest in a field of view of a video camera; generating, by a neural network, a pose vector associated with a pose of the object; comparing the pose vector to one or more clusters of pose vectors in a dataset of pose vectors, each cluster of pose vectors comprising one or more pose vectors generated from poses of objects; determining a value associated with the similarity of the pose vector to at least one of the one or more pose vectors in the cluster; comparing the value to a predetermined value; and based on the comparison of the value, adding the pose vector to the dataset or generating an alert.

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Bedford, MA, United States of America

Patrick Henson