Multimodal biometric recording architecture for the exploitation of applications in the context of affective computing

Abstract

In affective computing, it is important to design techniques that allow devices to acquire emotional states. To create and test these techniques it is necessary to have datasets that have several modalities namely, keystroke dynamics, electroencephalography, facial expressions, voice tone, heart rate, among others. This article presents a multimodal dataset that allowed us to detect the subjectivity that subsists in certain modalities -as are the surveys-and that is often overlooked, against objective modalities such as keystroke dynamics and electroencephalography. This article presents the creation of an environment in order to acquire a multimodal dataset. Work has also been done on the analysis of brain waves and their correspondence with other modalities.