Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method of determining a transformation is provided between an image coordinate system and an object coordinate system including: providing an object coordinate system associated with the object of interest, providing a 3D model of at least part of the object of interest, wherein the 3D model comprises 3D features, providing an N-th input depth image of at least part of the object of interest, wherein an N-th image coordinate system is associated with the N-th input depth image, providing an N-th plurality of 3D features in the N-th image coordinate system according to the N-th input depth image, estimating an N-th coarse transformation between the object coordinate system and the N-th image coordinate system according to a trained pose model and the N-th input depth image, and determining an N-th accurate transformation between the N-th image coordinate system and the object coordinate system.