Apple Inc.

Vinay Sharma

Patrick L Coffman

Elizabeth Caroline Cranfill

Hendrik Dahlkamp

Jonghoon Jin

Marco Zuliani

Nicholas M Fraioli

Craig Michael Federighi

Xiaosong Zhou

Xin Ru Wang

Andre Souza Dos Santos

Grant R Paul

Behkish J Manzari

Nathan De Vries

Steven D Baker

Ryan S Dixon

Johnnie B Manzari

Srinivasan Venkatachary

Vignesh Jagadeesh

Xiaojin Shi

James Nicholas Jones

Matthias Paulik

Neil Gareth Crane

Viktor Miladinov

Jeffrey A Brasket

Vincent M Lane

Francesco Rossi

Ke Zhang

Brandon J Corey

Alok Deshpande

Emilie Kim

Guillaume Borios

Xin Wang

Bartlomiej W Rymkowski

Nicholas D Lupinetti

Kellie L Albert

Nitin Gupta

Floris Chabert

Andrew C Edwards

Jingwen Zhu

Shujie Liu

Benjamin Poulain

Pulah J Shah

Ethan J Tira-Thompson

Yang Zhao

Matthias Dantone

Aravindhan K Krishnan

Mitchell Williams

Yaozong Gao

Abhishek Singh

Thomas Deselaers

Robert A Bailey

Kayur Dushyant Patel

Ron C Santos

Shuang Gao

Ngoc Hong Pham

Aya Siblini

Chen Ye

Francisco Alvaro Munoz

Osman G Sezer

Sergiy Buyanov

Ben Englert

Rajen Chatterjee

Amittai Axelrod

Siyang Tang

Jennifer Pon Chen

Sebastien V Marineau-Mes

Guangyu Zhong

Adam Huff Bradford

Xishuo Liu

Mallika Priya Khullar

Techniques are disclosed for determining whether to include a bodyprint in a cluster of bodyprints associated with a recognized person. For example, a device performs facial recognition to identify the identity of a first person. The device also identifies and stores physical characteristic information of the first person, the stored information associated with the identity of the first person based on the recognized face. Subsequently, the device receives a second video feed showing an image of a second person whose face is also determined to be recognized by the device. The device then generates a quality score for physical characteristics in the image of the user. The device can then add the image with the physical characteristics to a cluster of images associated with the person if the quality score is above a threshold, or discard the image if not.

Identity recognition utilizing face-associated body characteristics

Techniques are disclosed for providing a notification indicating an identity of a first person based on face-associated body characteristics. For example, a device performs facial recognition to identify the identity of the first person shown in a first video feed. The device also identifies and stores physical characteristic information of the first person from the first video feed, the stored information associated with the identity of the first person based on the recognized face. Subsequently, the device receives a second video feed showing a second person whose face is determined to not be recognized by the device. The device compares the stored physical characteristic information of the first person with additional physical characteristic information of the second person shown in the second video feed. Based on the comparison, the device provides a notification indicating whether the identity of the second person corresponds to the identity of the first person.

Techniques are disclosed for providing a notification that a person is at a particular location. For example, a resident device may receive from a user device an image that shows a face of a first person, the image being captured by a first camera of the user device. The resident device may also receive, from another device having a second camera, a second image showing a portion of a face of a second person, the second camera having a viewable area showing a particular location. The resident device may determine a score indicating a level of similarity between a first set of characteristics associated with the face of the first person and a second set of characteristics associated with the face of a second person. The resident device may then provide to the user device a notification based on determining the score.

Identifying objects within images from different sources

Methods and apparatuses are disclosed to personalize image capture operations of imaging equipment according to models that correspond uniquely to subjects being imaged. According to these techniques, a subject's face may be detected from a first image supplied by an image source and a first model of the subject may be developed from the detected face. The first model of the subject may be compared to another model of the subject developed from other images. Image adjustment parameters may be generated from a comparison of these models, which may control image adjustment techniques that are applied to the newly captured image of the subject. In this manner, aspects of the present disclosure may generate image capture operations that are tailored to characteristics of the subjects being imaged and avoid artifacts that otherwise could cause image degradation.

Image capture techniques personalized to individual subjects being imaged

A method includes receiving input data at a trained machine learning model that includes a common part and task-specific parts, receiving an execution instruction that identifies one or more processing tasks to be performed, processing the input data using the common part of the trained machine learning model to generate intermediate data, and processing the intermediate data using one or more of the task-specific parts of the trained machine learning model based on the execution instruction to generate one or more outputs.

Machine learning model with conditional execution of multiple processing tasks

The present disclosure generally relates to methods and user interfaces for managing visual content at a computer system. In some embodiments, methods and user interfaces for managing visual content in media are described. In some embodiments, methods and user interfaces for managing visual indicators for visual content in media are described. In some embodiments, methods and user interfaces for inserting visual content in media are described. In some embodiments, methods and user interfaces for identifying visual content in media are described. In some embodiments, methods and user interfaces for translating visual content in media are described.

User interfaces for managing visual content in media

Techniques disclosed for managing video captured by an imaging device. Methods disclosed capture a video in response to a capture command received at the imaging device. Following a video capture, techniques for classifying the captured video based on feature(s) extracted therefrom, for marking the captured video based on the classification, and for generating a media item from the captured video according to the marking are disclosed. Accordingly, the captured video may be classified as representing a static event, and, as a result, a media item of a still image may be generated. Otherwise, the captured video may be classified as representing a dynamic event, and, as a result, a media item of a video may be generated.

Automated media editing operations in consumer devices

The disclosure pertains to techniques for image processing. One such technique comprises a method for image selection, comprising: obtaining a sequence of images, detecting a first face in one or more images of the sequence of images, determining a first location for the detected first face in each of the images having the detected first face, generating a heat map based on the first location of the detected first face in each of the images of the sequences of images, determining a face quality score for the detected first face for each of the one or more images having the detected first face, determining a peak face quality score for the detected first face based in part on the face quality score and the generated heat map, and selecting a first image of the sequence of images, corresponding with the peak face quality score for the detected first face.

Face quality of captured images

Sharing media items includes receiving, on a first device, user input indicating a request for media items on a second device, wherein the request identifies an object represented in the media items, obtaining, on a requesting device, an object model for the object, generating a pull request comprising pull request parameters, wherein the pull request parameters comprise the object model, and transmitting the pull request from the first device to the second device, wherein the object model identifies the object represented in the media items.

Customizable, pull-based asset transfer requests using object models

Training a generative adversarial network (GAN) for use in facial recognition, comprising providing an input image of a particular face into a facial recognition system to obtain a faceprint; obtaining, based on the input faceprint and a noise value, a set of output images from a GAN generator; obtaining feedback from a GAN discriminator, wherein obtaining feedback comprises inputting each output image into the GAN discriminator and determining a set of likelihood values indicative of whether each output image comprises a facial image; determining, based on each output image, a modified noise value; inputting each output image into a second facial recognition network to determine a set of modified faceprints; defining, based on each modified noise value and modified faceprint, feedback for the GAN generator, wherein the feedback comprises a first value and a second value; and modifying control parameters of the GAN generator.

Face synthesis using generative adversarial networks

Managing a secure session includes detecting a login event at an electronic device using a first login method to initiate a secure session, capturing an initial image at a same time as the login event, capturing initial sensor data at the same time as the login event, monitoring for changes in the sensor data during the secure session, maintaining the secure session based on the initial sensor data and the monitored changes from the initial sensor data, and during the secure session, permitting access to the electronic device using reidentification.

System and method for person reidentification

Growing community of inventors

Palo Alto, CA, United States of America

Vinay Sharma