Apple Inc.

Feng Tang

Yang Yang

Thorsten Gernoth

Afshin Dehghan

Haitao Guo

Jonathan Pokrass

Xiaojin Shi

Kai Kang

Yikang Liao

Guangyu Zhao

Steven Porter Hotelling

Jerrold Von Hauck

Moshe Laifenfeld

Lynn Richard Youngs

Ivan Krstic

Anup K Sharma

Thomas P Mensch

Kelsey Yukio Ho

Michael Ross Malone

Lucia Elena Ballard

Matthew C Waldon

Tal Kaitz

Petr Kostka

Deepti S Prakash

Andrei Kolin

Christopher G Zeleznik

Ziv Hendel

Eitan Hirsh

Gideon Littwin

Chong Chen

Etai Littwin

Lucie Kucerova

Joshua M Susskind

Eslam A Mostafa

Atulit Kumar

Pavan Kumar Anasosalu Vasu

Shih-Yu Sun

Onur Canturk Hamsici

Micah P Kalscheur

Touraj Tajbakhsh

Hao Tang

Chen Huang

Ang Li

Joerg A Liebelt

Walter A Talbott

Walter A Talbot

Pavan Kumar Ansosalu Vasu

Bin Liu

Boyuan Sun

Fengfu Li

Chris Zeleznik

Deepti Sunder Prakash

Various implementations disclosed herein include devices, systems, and methods that generate floorplans and measurements using a three-dimensional (3D) representation of a physical environment generated based on sensor data.

Floorplan generation based on room scanning

Techniques are disclosed relating to biometric authentication, e.g., facial recognition. In some embodiments, a device is configured to verify that image data from a camera unit exhibits a pseudo-random sequence of image capture modes and/or a probing pattern of illumination points (e.g., from lasers in a depth capture mode) before authenticating a user based on recognizing a face in the image data. In some embodiments, a secure circuit may control verification of the sequence and/or the probing pattern. In some embodiments, the secure circuit may verify frame numbers, signatures, and/or nonce values for captured image information. In some embodiments, a device may implement one or more lockout procedures in response to biometric authentication failures. The disclosed techniques may reduce or eliminate the effectiveness of spoofing and/or replay attacks, in some embodiments.

Biometric authentication techniques

Scan-based measurements

Intelligent systems are disclosed that respond to user intent and desires based upon activity that may or may not be expressly directed at the intelligent system. In some embodiments, the intelligent system acquires a depth image of a scene surrounding the system. A scene geometry may be extracted from the depth image and elements of the scene may be monitored. In certain embodiments, user activity in the scene is monitored and analyzed to infer user desires or intent with respect to the system. The interpretation of the user's intent as well as the system's response may be affected by the scene geometry surrounding the user and/or the system. In some embodiments, techniques and systems are disclosed for interpreting express user communication, e.g., expressed through hand gesture movements. In some embodiments, such gesture movements may be interpreted based on real-time depth information obtained from, e.g., optical or non-optical type depth sensors.

Multi media computing or entertainment system for responding to user presence and activity

A facial recognition process operating on a device may include one or more processes that determine if a camera and/or components associated with the camera are obstructed by an object (e.g., a user's hand or fingers). Obstruction of the device may be assessed using flood infrared illumination images when a user's face is not able to be detected by a face detection process operating on the device. Obstruction of the device may also be assessed using a pattern detection process that operates after the user's face is detected by the face detection process. When obstruction of the device is detected, the device may provide a notification to the user that the device (e.g., the camera and/or an illuminator) is obstructed and that the obstruction should be removed for the facial recognition process to operate correctly.

Obstruction detection during facial recognition processes

Systems and processes for metric learning distillation are disclosed herein. In accordance with one example, a method includes, at an electronic device, at an electronic device having one or more processors and memory, receiving a first plurality of vectors from a first model, receiving a second plurality of vectors from a second model, determining a first plurality of vector distances based on the first plurality of vectors, generating a first matrix based on the first plurality of vector distances, determining a second plurality of vector distances based on the second plurality of vectors, generating a second matrix based on the second plurality of vector distances, comparing the first matrix with the second matrix, and adjusting the second model based on the comparison of the first matrix and the second matrix.

Model and ensemble compression for metric learning

A facial recognition authentication process operating on a device may capture an image of a user using a camera on the device. The facial recognition authentication process may include operating a full face facial recognition authentication process on the captured image or operating a partial face facial recognition authentication process on the captured image. The process may determine which process to operate (either full face or partial face) based on an assessment of an amount of occlusion in the captured image. The partial face facial recognition authentication process may be operated when there is at least some occlusion of selected features (e.g., nose and/or mouth) on the user's face in the captured image.

Periocular facial recognition switching

Techniques described herein provide efficient object detection and tracking in video images, such as may be used for real-time camera control in power-limited mobile image capture devices. The techniques include performing object detection on a first subset of frames of an input video, detecting an object and object location in a first detection frame of the first subset of frames, and tracking the detected object on a second subset of frames of the input video after the first detection frame.

Efficient object detection and tracking

An image captured using a camera on a device (e.g., a mobile device) may be operated on by one or more processes to determine properties of a user's face in the image. A first process may determine one or more first properties of the user's face in the image. A second process operating downstream from the first process may determine at least one second property of the user's face in the image. The second process may use at least one of the first properties from the first process to determine the second property.

Network performance by including attributes

Techniques described herein provide efficient object detection and tracking in video images, such as may be used for real-time camera control in power-limited mobile image capture devices. The techniques include performing object detection on a first subset of frames of an input video, detecting an object and object location in a first detection frame of the first subset of frames, and tracking the detected object on a second subset of frames of the input video after the first detection frame, wherein the second subset does not include frames of the first subset.

Efficient face detection and tracking

Growing community of inventors

Cupertino, CA, United States of America

Feng Tang