Intuit, Inc.

Jianxiang Chang

Yan Qu

Lin Tao

Srividya Narayanan

Hao Zheng

Sayan Paul

This disclosure provides systems, methods and apparatuses for machine learning. In some implementations, a pre-processing system may identify one or more special features in an input dataset and may generate one or more pruned datasets, respectively, based on the identified special features. The pre-processing system maps each of the pruned datasets to one or more predictions and selectively removes the values associated with one or more special features from the input dataset based on the mapping. In some other implementations, a pre-processing system may map each of the feature sets in an input dataset to multiple predictions in accordance with multiple machine learning algorithms. The pre-processing system evaluates a performance of each of the mappings and selects one of the machine learning algorithms to be used to train the machine learning model based on the performance of each mapping.

Feature pruning and algorithm selection for machine learning

A method including generating a captured facial object and a captured pose from a captured image. The method also includes obtaining a base facial object and a base pose from a base image. The method also includes generating base pose angles using the captured pose, and captured pose angles using the captured pose. The method also includes obtaining selected base images using the base pose angles and the base facial object. The method also includes generating selected captured images using the captured pose angles and the captured facial object. The method also includes comparing the selected base images to the selected captured images to establish a comparison. The method also includes outputting a match output using the comparison.

Large pose facial recognition based on 3D facial model

This disclosure relates to generating a comprehensive set of synthetic utterances. An example system is configured to provide an input utterance to a plurality of synthetic utterance generation pipelines in parallel. Each of the plurality of synthetic utterance generation pipelines include one or more utterance synthesizers. For example, one or more pipelines may use a synthesizer chain that includes a plurality of synthesizers in parallel. The plurality of synthetic utterance generation pipelines generates synthetic utterances, which may be stored in a database after evaluating the similarity between the original input utterance and each resulting synthetic utterance. For example, a synthetic utterance may be retained if the cosine similarity between the input and synthetic utterances is less than a predetermined threshold. Additionally, the synthetic utterances may be fed back at input utterances based on the similarity evaluation and the feedback loop repeated until a desired number of utterances are generated.

Synthetic utterance generation

Systems and methods for verifying an identity of a party to a video call are disclosed. An example method may be performed by one or more processors of a verification system and include storing first facial encodings, each of the first facial encodings associated with one or more images of a corresponding registered user of a plurality of registered users associated with the verification system, receiving, during a video call associated with a specified registered user of the plurality of registered users, a second facial encoding associated with one or more images of a person participating in the video call, the second facial encoding received from a device with which the person uses to participate in the video call, identifying, among the first facial encodings, a first facial encoding corresponding to the specified registered user, and determining whether the person participating in the video call is the specified registered user.

Large scale and high throughput video identity monitoring and verification

Systems and methods for verifying identifies of parties to a video conference call are disclosed. An example method includes storing a plurality of first facial encodings each associated with one or more images of a registered agent, receiving, during the video conference call, a second facial encoding associated with one or more images of a person participating in the video conference call, determining that a specified registered agent is scheduled as a party to the video conference call, comparing the second facial encoding to a selected first facial encoding associated with the specified registered agent, and determining, based at least in part on the comparing, whether or not the person is the specified registered agent.

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Mountain View, CA, United States of America

Jianxiang Chang