Samsung Electronics Co., Ltd.

Amazon Technologies, Inc.

Lior Zamir

Gerard Guy Medioni

Manoj Aggarwal

Nathan Henri Levy

Igor Kviatkovsky

Nadav Israel Bhonker

Dilip Kumar

Alon Shoshan

Sebastien Derhy

Prithviraj Banerjee

Miriam Farber

Zheng Tang

Ori Linial

A method for simultaneous localization and mapping (SLAM) employs dual event-based cameras. Event streams from the cameras are processed by an image processing system to stereoscopically detect surface points in an environment, dynamically compute pose of a camera as it moves, and concurrently update a map of the environment. A gradient descent based optimization may be utilized to update the pose for each event or for each small batch of events.

Simultaneous location and mapping (SLAM) using dual event cameras

An image of at least a portion of a user during enrollment to a biometric identification system is acquired and processed with a plurality of models to determine embeddings that are representative of features in that image in respective embedding spaces. Transformers are trained to accept as input an embedding from a respective model and produce as output a transformed embedding consistent with a common embedding space. These transformed embeddings are aggregated to produce transformed aggregated embedding data and stored for later comparison to identify the user. Subsequently, one or more embedding models may be used to process a query image to determine a query embedding. The query embedding may be transformed into the common embedding space for comparison.

System to reduce data retention using aggregated embedding data

A user performs an enrollment process to utilize a biometric identification system. This includes acquisition of biometric input data. Accuracy of subsequent identification is improved by utilizing high quality input data during enrollment. Input data is processed using a plurality of embedding models to determine a plurality of embedding vectors. These embedding vectors are translated into a common embedding space. Input quality may be determined based on analysis of these embedding vectors. For example, if a mean distance of the translated embedding vectors is less than a threshold value, the input data may be deemed to be of sufficient quality for use to complete an enrollment process. This analysis may also be used for post-enrollment operation, such as during an identification process to determine query input data that is of insufficient quality.

System to assess biometric input data

An image of at least a portion of a user during enrollment to a biometric identification system is acquired and processed with a first model to determine a first embedding that is representative of features in that image in a first embedding space. The first embedding may be stored for later comparison to identify the user, while the image is not stored. A second model that uses a second embedding space may be later developed. A transformer is trained to accept as input an embedding from the first model and produce as output an embedding consistent with the second embedding space. The previously stored first embedding may be converted to a second embedding in a second embedding space using the transformer. As a result, new embedding models may be implemented without requiring storage of user images for later reprocessing with the new models or requiring re-enrollment by users.

System to reduce data retention

This disclosure describes techniques for identifying users that are enrolled for use of a user-recognition system and updating identification data of these users over time. To enroll in the user-recognition system, the user may initially scan his or her palm. The resulting image data may later be used when the user requests to be identified by the system by again scanning his or her palm. However, because the characteristics of user palms may change over the time, the user-recognition system may periodically perform processes for updating the identification data stored in association with the user in order to maintain or increase an accuracy of the user-recognition system.

Updating identification data in automated user-identification systems

Simultaneous localization and mapping (SLAM) using dual event cameras

A scanner acquires a set of images of a hand of a user to facilitate identification. These images may vary, due to changes in relative position, pose, lighting, obscuring objects such as a sleeve, and so forth. A first neural network determines output data comprising a spatial mask and a feature map for individual images in the set. The output data for two or more images is combined to provide aggregate data that is representative of the two or more images. The aggregate data may then be processed using a second neural network, such as convolutional neural network, to determine an embedding vector. The embedding vector may be stored and associated with a user account. At a later time, images acquired from the scanner may be processed to produce an embedding vector that is compared to the stored embedding vector to identify a user at the scanner.

System for determining embedding from multiple inputs

Event-based image feature extraction includes reducing an accumulated magnitude of a leaky integrate and fire (LIF) neuron based on a difference between a current time and a previous time; receiving an event input from a dynamic vision sensor (DVS) pixel at the current time; weighting the received input; adding the weighted input to the reduced magnitude to form an accumulated magnitude of the LIF neuron at the current time; and, if the accumulated magnitude reaches a threshold, firing the neuron and decreasing the accumulated magnitude.

Event-based image feature extraction

A method for acquiring intensity images through a dynamic vision sensor is provided, the method including resetting an inner state of each of a plurality of pixels in the dynamic vision sensor to a predetermined value; receiving from at least one of the plurality of pixels an event signal based on a change in its inner state indicative of a difference in amount of light incident upon the at least one of the plurality of pixels versus that corresponding to the predetermined value; and integrating over a time period the received event signal for the at least one of the plurality of pixels to determine the intensity of light incident upon the at least one of the plurality of pixels.

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Ramat Hasharon, Israel

Lior Zamir