Apple Inc.

Pavan Kumar Anasosalu Vasu

Steven Porter Hotelling

Jerrold Von Hauck

Steve Porter Hotelling

Moshe Laifenfeld

Lynn Richard Youngs

Ivan Krstic

Anup K Sharma

Jonathan Pokrass

Thorsten Gernoth

Feng Tang

Thomas P Mensch

Michael Ross Malone

Matthew C Waldon

Lucia Elena Ballard

Tal Kaitz

Petr Kostka

Andrei Kolin

Deepti S Prakash

Christopher G Zeleznik

Ziv Hendel

Gideon Littwin

Eitan Hirsh

Etai Littwin

Lucie Kucerova

Thilaka S Sumanaweera

Kelsey Yukio Ho

Cuneyt Oncel Tuzel

David J Feathers

Shreyas Saxena

Gopal Valsan

Liliana I Keats

Deepti Sunder Prakash

Chris Zeleznik

An electronic device may include body composition analysis circuitry that estimates body composition based on captured images of a face, neck, and/or body (e.g., depth map images captured by a depth sensor, visible light and infrared images captured by image sensors, and/or other suitable images). The body composition analysis circuitry may analyze the image data and may extract portions of the image data that strongly correlate with body composition, such as portions of the cheeks, neck, waist, etc. The body composition analysis circuitry may encode the image data into a latent space. The latent space may be based on a deep learning model that accounts for facial expression and neck pose in face/neck images and that accounts for breathing and body pose in body images. The body composition analysis circuitry may output an estimated body composition based on the image data and based on user demographic information.

Electronic devices with body composition analysis circuitry

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

A method includes determining a detection output that represents an object in a two-dimensional image using a detection model, wherein the detection output includes a shape definition that describes a shape and size of the object; defining a three-dimensional representation based on the shape definition, wherein the three-dimensional representation includes a three-dimensional model that represents the object that is placed in three-dimensional space according to a position and a rotation; determining a three-dimensional detection loss that describes a difference between the three-dimensional representation and three-dimensional sensor information; and updating the detection model based on the three-dimensional detection loss.

Object detection with position, pose, and shape estimation

Growing community of inventors

Sunnyvale, CA, United States of America

Pavan Kumar Anasosalu Vasu