Abstract: A method of tracking a user's toothcare activity comprises receiving video images of a user's face during, e.g. a toothbrushing session, and identifying, in each of a plurality of frames of the video images, predetermined features of the user's face. The features include at least two invariant landmarks associated with the user's face and one or more landmarks selected from at least mouth feature positions and eye feature positions. Predetermined marker features of a toothcare appliance, e.g. a brush in use are identified in each of the plurality of frames of the video images. From the at least two invariant landmarks associated with the user's nose, a measure of inter-landmark distance is determined. An appliance length normalised by the inter-landmark distance is determined. From the one or more landmarks selected from at least mouth feature positions and eye feature positions, one or more appliance-to-facial feature distances each normalised by the inter-landmark distance is determined.

Abstract: Described is a task for an automobile interior having at least one subject that creates a video input, an audio input, and a context descriptor input. The video input relates to the at least one subject and is processed by a face detection module and a facial point registration module to produce a first output. The first output is further processed by at least one of: a facial point tracking module, a head orientation tracking module, a body tracking module, a social gaze tracking module, and an action unit intensity tracking module. The audio input relating to the at least one subject is processed by a valence and arousal affect states tracking module to produce a second output and to produce a valence and arousal scores output. A temporal behavior primitives buffer produce a temporal behavior output. Based on the foregoing, a mental state prediction module predicts the mental state of at least one subject in the automobile interior.

Abstract: A computing system comprising a generative neural network (100) is disclosed. The generative neural network (100) is configured to receive a plurality of input videos. Each input video comprises a face defining an identity. Each input video comprises a behaviour. Each input video comprises the same behaviour as each of the other input videos, and a different identity to each of the other input videos. The generative neural network (100) is also configured synthesize an output video from the input videos. The output video comprises a synthetic face defining a synthetic identity. The generative neural network has been trained, with a loss function, to preserve the behaviour of the input videos while generating the synthetic identity that is different from each identity of the input videos.

Abstract: Technologies for analyzing multi-task multimodal data to detect multi-task multimodal events using a deep multi-task representation learning, are disclosed. A combined model with both generative and discriminative aspects is used to share information during both generative and discriminative processes. The technologies can be used to classify data and also to generate data from classification events. The data can then be used to morph data into a desired classification event.