Amazon Technologies, Inc.

Prithviraj Banerjee

Gerard Guy Medioni

Manoj Aggarwal

Dilip Kumar

Rajeev Ranjan

Zheng Tang

Jean Laurent Guigues

Leonid Pishchulin

Korwin Jon Smith

Ammar Chinoy

Emilio Ian Maldonado

Matthew Christopher Smith

David Allen Smith

Lior Zamir

Pranab Mohanty

Douglas Andrew Hungarter

Joachim Sebastian Stahl

Brad Musick

Lavisha Aggarwal

Nichole Stockman

Joshua Adam Cowan

Jared Corso

Jiuhong Xiao

During enrollment to a biometric identification system an image of at least a portion of a user is acquired and processed to determine a compact representation (CR). The CR may be stored for later comparison to identify the user, to train embedding models, and so forth. The image is not stored. A machine learning system that comprises a variational autoencoder is trained to produce the CR with a loss function that includes a distortion loss, an embedding distance loss, and in some implementations a bitrate loss. The trained encoder is used to determine the CR from an input image, while the decoder is not stored. The CR contains sufficient information to be used as training data for embedding models, instead of acquired images. Training may be more computationally efficient using the CR. Recognition comparisons may be more efficiently performed using the CR, compared to query images.

System to determine compact representation of data

This disclosure describes a user-recognition system that may perform one or more verification methods upon identifying a previous image that matches a current image of a palm of a user. For instance, the user-recognition system may perform the verification method(s) as part of the recognition method (e.g., after recognizing a matching image), in response to an audit process, in response to a request to re-analyze the image data (e.g., because a user indicates that he or she was not associated with a particular purchase or shopping session), and/or the like.

Utilizing sensor data for automated user identification

This disclosure describes techniques for providing instructions when receiving biometric data associated with a user. For instance, a device may detect a portion of a user, such as a hand. The device may then display a first graphical element indicating a target location for placing the portion of the user above the user-recognition device. Additionally, the device may determine locations of the portion of the user above the device. The device may then display a second graphical element indicating the locations, such as when the locations are not proximate to the target location. Additionally, the device may display instructions for moving the portion of the user to the target location. Based on detecting that the location of the portion of the user is proximate to the target location, the device may send data representing the portion of the user to a remote system.

Electronic device for automated user identification

A neural network is trained to process input data and generate a classification value that characterizes the input with respect to an ordered continuum of classes. For example, the input data may comprise an image and the classification value may be indicative of a quality of the image. The ordered continuum of classes may represent classes of quality of the image ranging from 'worst', “bad”, “normal”, “good”, to “best”. During training, loss values are determined using an ordered classification loss function. The ordered classification loss function maintains monotonicity in the loss values that corresponds to placement in the continuum. For example, the classification value for a “bad” image will be less than the classification value indicative of a “best” image. The classification value may be used for subsequent processing. For example, biometric input data may be required to have a minimum classification value for further processing.

System for training neural network using ordered classes

Actors may be detected and tracked within a scene using multiple imaging devices provided in a network that are aligned with fields of view that overlap at least in part. Processors operating on the imaging devices may evaluate the images using one or more classifiers to recognize body parts within the images, and to associate the body parts with a common actor within the scene. Each of the imaging devices may generate records of the positions of the body parts and provide such records to a central server, that may correlate body parts appearing within images captured by two or more of the imaging devices and generate a three-dimensional model of an actor based on positions of the body parts. Motion of the body parts may be tracked in subsequent images, and the model of the actor may be updated based on the motion.

Generating tracklets from digital imagery

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

Techniques for an identity-verification system to analyze image data representing palms of users using a segmented, characteristic-based approach. The system may compare palm-feature data representing characteristics of a palm of a user (or 'query palm') with stored palm-feature data of palms for user profiles (or 'stored palms'). For instance, the system may identify characteristics of the query palm having salient or discriminative features, and compare palm-feature data for those discriminative characteristics to palm-feature data representing corresponding characteristics of stored palms of enrolled users. Additionally, the system may compare characteristics of the query palm with corresponding characteristics of stored palms until the system is confident that the query palm corresponds to a stored palm of a user profile. By performing simpler characteristic-by-characteristic sameness verification tasks, the system may reduce the amount of time and computing resources utilized to verify an identity of a user as opposed to top-level, palm-identity verification.

Described is a multiple-camera system and process for re-identifying a user located in a materials handling facility based on user patterns and/or descriptors representative of the user. In one implementation, a user pattern and/or a plurality of descriptors representative of a user are maintained as a position of a user is tracked through a materials handling facility. If the tracking of the user is lost, the last known position is stored with the user pattern and/or descriptors. If a new object is detected and confirmed to be a user, a user pattern and/or descriptors of the new object are compared with the stored user pattern and/or descriptors to determine if the new object is the user.

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Redmond, WA, United States of America

Prithviraj Banerjee