Electroencephalogram (EEG) has been widely used for the detection of anxiety because of its ability to reflect the functional activities of the brain. However, EEG alone may not provide precision in the detection of anxiety because other emotional diso…
In real-world applications such as emotion recognition from recorded brain activity, data are captured from electrodes over time. These signals constitute a multidimensional time series. In this article, Echo State Network (ESN), a recurrent neural net…
Inspite the emerging importance of Speech Emotion Recognition (SER), the state-of-the-art accuracy is quite low and needs improvement to make commercial applications of SER viable. A key underlying reason for the low accuracy is the scarcity of emotion…
The domain of Aspect-based Sentiment Analysis, in which aspects are extracted, their sentiments are analysed and sentiments are evolved over time, is getting much attention with increasing feedback of public and customers on social media. The immense a…
In an intelligent surveillance system, detecting and predicting diverse collective crowd behaviors has emerged as a challenging problem for efficient crowd management. In real-world scenarios, potential disasters and hazards can be averted by consideri…
This article introduces BReG-NeXt, a residual-based network architecture using a function wtih bounded derivative instead of a simple shortcut path (a.k.a. identity mapping) in the residual units for automatic recognition of facial expressions based on…
Micro-expression is a spontaneous expression that occurs when a person tries to mask his or her inner emotion, and can neither be forged nor suppressed. It is a kind of short-duration, low-intensity, and usually local-motion facial expression. However,…
With the increasing development of advanced unmanned aerial vehicles (UAVs), communication between operators and these intelligent systems is becoming more stressful. For the safety of UAV flights, automatic psychological stress detection is becoming a key research topic for successful missions. Stress can be reliably estimated via some biological markers which are not appropriate in many cases of human-machine-interaction setups. In this article, we propose a non-intrusive deep learning-based stress level estimation approach. The goal is to identify the region where the operator’s emotional state projects in the space defined by the latent dimensional emotions of arousal and valence since the stress region is well delimited in this space. The proposed multimodal approach uses sequential temporal CNN and LSTM with an Attention Weighted Average layer in the vision modality. As a second modality, we investigate local and global descriptors such as Mel-frequency cepstral coefficients, i-vector embeddings as well as Fisher-vector encodings. The multimodal-fusion approach uses a strategy referred to as “late-fusion” that involves the combination of unimodal model outputs as inputs of the decision engine. Since we have to deal with more naturalistic behavior in operator-machine interaction contexts, the One minute Gradual Emotion Challenge dataset was used for predictive model validation.
In this paper, we propose a novel Electroencephalograph (EEG) emotion recognition method inspired by neuroscience with respect to the brain response to different emotions. The proposed method, denoted by R2G-STNN, consists of spatial and temporal neura…
Automatic prediction of emotion promises to revolutionise human-computer interaction. Recent trends involve fusion of multiple data modalities $-$- audio, visual, and physiological $-$- to classify emotional state. However, in practice, collection of p…