Affective computing studies and develops systems capable of detecting humans affects. The search for universal well-performing features for speech-based emotion recognition is ongoing. In this paper, a small set of features ...

This paper presents a multimodal emotion recognition system, which is based on the analysis of audio and visual cues. From the audio channel, Mel-Frequency Cepstral Coefficients, Filter Bank Energies and prosodic features ...

Multi-view plus-depth-map (MVD) video streaming with autostereoscopic displays provides multi-user immersive media experiences. In this context, delivery of MVD representation to multiple clients remains a challenging ...

This work proposes a fully convolutional network architecture for RGB face image generation from a given input thermal face image to be applied in face recognition scenarios. The proposed method is based on the FusionNet ...

Recognition of facial micro-expressions (MEs), which indicates conscious or unconscious suppressing of true emotions, is still a challenging task in the affective computing and computer vision. There are two main reasons ...

In this research work, machine learning techniques are used to classify magnetic resonance imaging brain scans of people with Alzheimer's disease. This work deals with binary classification between Alzheimer's disease and ...

Action recognition is a challenging task that plays an important role in many robotic systems, which highly depend on visual input feeds. However, due to privacy concerns, it is important to find a method which can recognise ...

We address multimodal product attribute prediction of fashion items based on product images and titles. The product attributes, such as type, sub-type, cut or fit, are in a chain format, with previous attribute values ...

After the publication of this work [1], it was noticed that the co-author Pejman Rasti was omitted as the first author following an institutional investigation. The first author’s name is inserted in this erratum article.