Abstract: Related methods are provided for establishing a baseline value to represent an eyelid opening dimension for a person engaged in an activity, where the activity may be driving a vehicle, operating industrial equipment, or performing a monitoring or control function; and for operating a system for monitoring eyelid opening values with real time video data.

Abstract: Tracking units for facial features with advanced training for natural rendering of human faces in real-time are provided. An example device receives a video stream, and upon detecting a visual face, selects a 3D model from a comprehensive set of head orientation classes. The device determines modifications to the selected 3D model to describe the face, then projects a 2D model of tracking points of facial features based on the 3D model, and controls, actuates, or animates hardware based on the facial features tracking points. The device can switch among an example comprehensive set of 35 different head orientation classes for each video frame, based on suggestions computed from a previous video frame or from yaw and pitch angles of the visual head orientation. Each class of the comprehensive set is trained separately based on a respective collection of automatically marked images for that head orientation class.

Abstract: Related methods are provided for establishing a baseline value to represent an eyelid opening dimension for a person engaged in an activity, where the activity may be driving a vehicle, operating industrial equipment, or performing a monitoring or control function; and for operating a system for monitoring eyelid opening values with real time video data.

Abstract: Related methods are provided for establishing a baseline value to represent an eyelid opening dimension for a person engaged in an activity, where the activity may be driving a vehicle, operating industrial equipment, or performing a monitoring or control function; and for operating a system for monitoring eyelid opening values with real time video data.

Abstract: Tracking units for facial features with advanced training for natural rendering of human faces in real-time are provided. An example device receives a video stream, and upon detecting a visual face, selects a 3D model from a comprehensive set of head orientation classes. The device determines modifications to the selected 3D model to describe the face, then projects a 2D model of tracking points of facial features based on the 3D model, and controls, actuates, or animates hardware based on the facial features tracking points. The device can switch among an example comprehensive set of 35 different head orientation classes for each video frame, based on suggestions computed from a previous video frame or from yaw and pitch angles of the visual head orientation. Each class of the comprehensive set is trained separately based on a respective collection of automatically marked images for that head orientation class.

Abstract: Method for monitoring eyelid opening values. In one embodiment video image data is acquired with a camera which data are representative of a person engaged in an activity, where the activity may be driving a vehicle, operating industrial equipment, or performing a monitoring or control function. When the person's head undergoes a change in yaw angle, such that eyelids of both eyes of the person are captured with the camera, but one eye is closer to the camera than the other eye, a weighting factor is applied, which factor varies as a function of the yaw angle such that a value representative of eyelid opening data based on both eyes is calculated.

Abstract: Tracking units for facial features with advanced training for natural rendering of human faces in real-time are provided. An example device receives a video stream, and upon detecting a visual face, selects a 3D model from a comprehensive set of head orientation classes. The device determines modifications to the selected 3D model to describe the face, then projects a 2D model of tracking points of facial features based on the 3D model, and controls, actuates, or animates hardware based on the facial features tracking points. The device can switch among an example comprehensive set of 35 different head orientation classes for each video frame, based on suggestions computed from a previous video frame or from yaw and pitch angles of the visual head orientation. Each class of the comprehensive set is trained separately based on a respective collection of automatically marked images for that head orientation class.

Abstract: Tracking units for facial features with advanced training for natural rendering of human faces in real-time are provided. An example device receives a video stream, and upon detecting a visual face, selects a 3D model from a comprehensive set of head orientation classes. The device determines modifications to the selected 3D model to describe the face, then projects a 2D model of tracking points of facial features based on the 3D model, and controls, actuates, or animates hardware based on the facial features tracking points. The device can switch among an example comprehensive set of 35 different head orientation classes for each video frame, based on suggestions computed from a previous video frame or from yaw and pitch angles of the visual head orientation. Each class of the comprehensive set is trained separately based on a respective collection of automatically marked images for that head orientation class.

Abstract: Related methods are provided for establishing a baseline value to represent an eyelid opening dimension for a person engaged in an activity, where the activity may be driving a vehicle, operating industrial equipment, or performing a monitoring or control function; and for operating a system for monitoring eyelid opening values with real time video data.

Abstract: Method for monitoring eyelid opening values. In one embodiment video image data is acquired with a camera which data are representative of a person engaged in an activity, where the activity may be driving a vehicle, operating industrial equipment, or performing a monitoring or control function. When the person's head undergoes a change in yaw angle, such that eyelids of both eyes of the person are captured with the camera, but one eye is closer to the camera than the other eye, a weighting factor is applied, which factor varies as a function of the yaw angle such that a value representative of eyelid opening data based on both eyes is calculated.

Abstract: A method for generating a set of annotated images comprises acquiring a set of images of a subject, each acquired from a different point of view; and generating a 3D model of at least a portion of the subject, the 3D model comprising a set of mesh nodes defined by respective locations in 3D model space and a set of edges connecting pairs of mesh nodes as well as texture information for the surface of the model. A set of 2D renderings is generated from the 3D model, each rendering generated from a different point of view in 3D model space including providing with each rendering a mapping of x,y locations within each rendering to a respective 3D mesh node. A legacy detector is applied to each rendering to identify locations for a set of detector model points in each rendering. The locations for the set of detector model points in each rendering and the mapping of x,y locations provided with each rendering are analysed to determine a candidate 3D mesh node corresponding to each model point.