Abstract: Various implementations disclosed herein include devices, systems, and methods that provide a 3D room plan that combines 2D shapes representing elements of a room that are approximately planar (e.g., walls, wall openings, windows, doors, etc.) with 3D primitives representing non-planar elements (e.g., tables, chairs, appliances, etc.). A 3D room plan is a 3D representation of a room or other physical environment that generally identifies or otherwise represents 3D positions of one or more walls, floors, ceilings, other boundaries, other regions, windows, doors, openings, and 3D objects (e.g., objects having significant height, width, and depth in 3 dimensions) within the environment. For example, a 3D floor plan using 2D shapes to represent walls, windows, doors, etc. may be combined with 3D primitives such as 3D bounding boxes representing 3D objects to form a 3D room plan.

Abstract: 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.

Abstract: Detection of a user paying attention to a device may be used to enable or support biometric security (e.g., facial recognition) enabled features on the device. Images captured by a camera on the device may be used to determine if the user is paying attention to the device. Facial features of the user's face in the images may be assessed to determine if the user is paying attention to the device. Facial features may be assessed through comparison of feature vectors generated from the captured image feature to a set of known feature vectors. The known feature vectors for attention may be generated using a machine learning process.

Abstract: 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.

Abstract: Detection of a user paying attention to a device may be used to enable or support biometric security (e.g., facial recognition) enabled features on the device. Images captured by a camera on the device may be used to determine if the user is paying attention to the device. Facial features of the user's face in the images may be assessed to determine if the user is paying attention to the device. Facial features may be assessed through comparison of feature vectors generated from the captured image feature to a set of known feature vectors. The known feature vectors for attention may be generated using a machine learning process.

Abstract: An automatic learning method for the automatic learning of the forms of appearance of objects in images in the form of object features from training images for using the learned object features in an image processing system comprises determining a feature contribution by a training image to object features by weighted summation of training image features by means of linear filter operations, applied to the feature image, by using a weight image obtained at least from an annotation image and a classification image. This allows faster learning processes and also the learning of a greater variance of forms of appearance of objects and backgrounds, which increases the robustness of the system in its application to untrained images.