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: A computer implemented cognitive state estimation method (10) comprising: receiving a recording of a user and at least one context primitive (11), each context primitive comprising a time series of context descriptors; extracting at least one behaviour primitive from the recording (12), each behaviour primitive comprising a time series of behaviour descriptors; producing a behaviour map from the at least one behaviour primitive (13); producing a context map from the at least one context primitive (14); and estimating a cognitive state of the user using data derived from the behaviour map and the context map (15).

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.