公开(公告)号：US12300020B1

公开(公告)日：2025-05-13

申请号：US17809237

申请日：2022-06-27

Applicant: AMAZON TECHNOLOGIES, INC.

Inventor： Alon Shoshan ,  Nadav Israel Bhonker ,  Igor Kviatkovsky ,  Manoj Aggarwal ,  Gerard Guy Medioni ,  Lior Zamir ,  Ori Linial

IPC: G06V40/12 ,  G06F21/32 ,  G06V40/10

Abstract: 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.

公开(公告)号：US12086225B1

公开(公告)日：2024-09-10

申请号：US17448437

申请日：2021-09-22

Applicant: AMAZON TECHNOLOGIES, INC.

Inventor： Gerard Guy Medioni ,  Manoj Aggarwal ,  Alon Shoshan ,  Igor Kviatkovsky ,  Nadav Israel Bhonker ,  Lior Zamir ,  Dilip Kumar

IPC: G06F21/32 ,  G06F18/213 ,  G06F18/214 ,  G06F21/62

CPC classification number: G06F21/32 ,  G06F18/213 ,  G06F18/214 ,  G06F21/6245

Abstract: 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.

公开(公告)号：US11714877B1

公开(公告)日：2023-08-01

申请号：US17038463

申请日：2020-09-30

Applicant: AMAZON TECHNOLOGIES, INC.

Inventor： Alon Shoshan ,  Miriam Farber ,  Nadav Israel Bhonker ,  Igor Kviatkovsky ,  Manoj Aggarwal ,  Gerard Guy Medioni

IPC: G06K9/62 ,  G06N3/08 ,  G06N3/04 ,  G06V10/145 ,  G06V40/13 ,  G06N3/088 ,  G06V40/12 ,  G06N3/045 ,  G06F18/214

CPC classification number: G06F18/214 ,  G06N3/0454 ,  G06N3/088 ,  G06V10/145 ,  G06V40/1318 ,  G06V40/1347

Abstract: A machine learning system to determine an identity of a user is trained using triplets of ad hoc synthetic data and actual data. The data may comprise multimodal images of a hand. Each triplet comprises an anchor, a positive, and a negative image. Synthetic triplets for different synthesized identities are generated on an ad hoc basis and provided as input during training of the machine learning system. The machine learning system uses a pairwise label-based loss function, such as a triplet loss function during training. Synthetic triplets may be generated to provide more challenging training data, to provide training data for categories that are underrepresented in the actual data, and so forth. The system uses substantially less memory during training, and the synthetic triplets need not be retained further reducing memory use. Ongoing training is supported as new actual triplets become available, and may be supplemented by additional synthetic triplets.

公开(公告)号：US11537813B1

公开(公告)日：2022-12-27

申请号：US17038648

申请日：2020-09-30

Applicant: AMAZON TECHNOLOGIES, INC.

Inventor： Igor Kviatkovsky ,  Nadav Israel Bhonker ,  Alon Shoshan ,  Manoj Aggarwal ,  Gerard Guy Medioni

IPC: G06K9/62 ,  G06N20/00 ,  G06V10/145 ,  G06V40/13 ,  G06V40/12

Abstract: During a training phase, a first machine learning system is trained using actual data, such as multimodal images of a hand, to generate synthetic image data. During training, the first system determines latent vector spaces associated with identity, appearance, and so forth. During a generation phase, latent vectors from the latent vector spaces are generated and used as input to the first machine learning system to generate candidate synthetic image data. The candidate image data is assessed to determine suitability for inclusion into a set of synthetic image data that may be used for subsequent use in training a second machine learning system to recognize an identity of a hand presented by a user. For example, the candidate synthetic image data is compared to previously generated synthetic image data to avoid duplicative synthetic identities. The second machine learning system is then trained using the approved candidate synthetic image data.