Publications by Vittorio Cuculo

In this note, we address the problem of simulating electromyographic signals arising from muscles involved in facial expressions - markedly those conveying affective information -, by relying solely on facial landmarks detected on video sequences. We propose a method that uses the framework of Gaussian Process regression to predict the facial electromyographic signal from videos where people display non-posed affective expressions. To such end, experiments have been conducted on the OPEN EmoRec II multimodal corpus.

This note gives a preliminary account of the transcoding or rechanneling problem between different stimuli as it is of interest for the natural interaction or affective computing fields. By the consideration of a simple example, namely the color response of an affective lamp to a sensed facial expression, we frame the problem within an information-theoretic perspective. A full justification in terms of the Information Bottleneck principle promotes a latent affective space, hitherto surmised as an appealing and intuitive solution, as a suitable mediator between the different stimuli.

In this article we address the issue of adopting a local sparse coding representation (Histogram of Sparse Codes), in a part-based framework for inferring the locations of facial landmarks. The rationale behind this approach is that unsupervised learning of sparse code dictionaries from face data can be an effective approach to cope with such a challenging problem. Results obtained on the CMU Multi-PIE Face dataset are presented providing support for this approach.