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Using color histograms in automatic emotion recognition systems faces different issues. One of the important challenges is to determine the appropriate number of bins in the color histogram to achieve the highest recognition performance possible with minimal computations. This research focuses on emotion recognition induced by visual contents of images, or REVC for short, using ARTphoto dataset. Twenty-two...
In recent years, most breakthroughs in fields such as image and video processing were based on machine learning technologies that allow computers to recognize objects in images with nearly human precision. In some application domains, computers even surpassed human level performance. These breakthroughs result from an exponential increase of computational resources and digitization of society (massive...
Facial analysis in videos and images has been a relatively tough task for machine learning models. Recent use of deep learning approaches has demonstrated substantial improvement in results and reliability and can be used for problems such as face recognition, emotion recognition and emotion reaction prediction. In the case of emotion reaction, relevant information of emotions in individual frames...
Emotion recognition is a rapidly growing research domain in recent years. Unlike humans, machines lack the abilities to perceive and show emotions. But human-computer interaction can be improved by automated emotions recognition, thereby reducing the need of human intervention. In this paper, four basic emotions (Anger, Happy, Fear and Neutral) are analyzed from emotional speech signals. Signal processing...
Emotion recognition using EEG signals has become a hot research topic in the last few years. This paper aims at providing a novel method for emotion recognition using less channels of frontal EEG signals. By employing the asymmetry theory of frontal brain, a new method fusing spatial and frequency features was presented, which only adopted two channels of frontal EEG signals at Fp1 and Fp2. In order...
The brain produces weak electrical signals that can be measured from the skull. Electroencephalography (EEG) is a method that provides monitoring electrical activity of the brain with the electrical methods. Brain Computer Interface (BCI) is a system that converts the electrical signals produced by the brain to the signals that can be interpreted by a computer or an electronic system. Brain Computer...
In this paper, we try to recognize negative emotions (sadness and disgust) of human affecting driving by using physiological signals that are commonly used to deal with human emotions. To do this, emotional stimuli are used to induce sadness and disgust, and emotion recognition is performed based on the feature vector extracted from the physiological signals collected on the induced emotion by a stimulus...
An emotion recognition method from the face images is proposed in this paper, which can recognize seven emotions of human, i.e., six basic expressions in addition to neutral. The proposed method uses the GLCM approach for feature extraction and the nearest neighbor (NN) for classification. The fuzzy Euclidean distance is used. GLCM provides the texture characteristics of an input image through the...
For the sake of improving the precision of speech emotion recognition, this paper proposed a novel speech emotion recognition approach based on Gaussian Kernel Nonlinear Proximal Support Vector Machine (PSVM) to recognize four basic human emotions (angry, joy, sadness, surprise). Firstly, preprocess speech signal containing sampling, quantification, pre-emphasizing, framing, adding window and endpoint...
In some of the EEG-based recognition tasks, for example, EEG-based emotion recognition (EEG-ER), enhancing feature extractors is difficult. In such cases, the use of deep neural networks which are capable of classification and recognition by the input of raw data is desirable. Therefore, effective components and models of neural networks for EEG-based recognition must be proposed. In addition, the...
Emotion recognition is important at the workplace because it impacts a multitude of outcomes, such as performance, engagement and well-being. Emotion recognition from audio is an attractive option due to its non-obtrusive nature and availability of microphones in devices at the workplace. We describe building a classifier that analyzes the para-linguistic features of audio streams to classify them...
Speech emotion recognition has been widely used in human computer interaction and applications. This paper has classified emotion into two classes: happy and angry. All the speech signal is preprocessed from Malay spoken speech database. Emotional information is obtained by applying two well-established acoustical features that are Mel Frequency Cepstral Coefficients (MFCC) and Short Time Energy (STE)...
At present, most of the EEG emotion recognition studies have taken all electric shocks or filtered electrodes as a feature and they are integrated (combined) with simple features that are extracted from other signals as a single classifier Emotional classification, but there are problems such as low efficiency and low accuracy. Aiming at this problem, this paper proposes an EEG emotion classification...
Emotional recognition as the key technology in the field of emotion computing has received more and more attentions in applications such as human-computer interaction, medical-assisted diagnosis and multimedia intelligence recommendation, and it has important research and application value. EEG recognition based on EEG is a commonly used and effective method of emotion recognition. More and more scholars...
Human machine interaction fieldhas potentialapplications in different domainssuch as medicine therapies for vulnerable persons. Thus, allowing the machine to identify and understand emotional states is one of the primordial stages for affective interactivity with Humans. Recent studies have proved that physiological signals contribute to recognize the emotion. In this paper, we aim to classify the...
One of the objectives of this research is to explore and investigate on how to improve online voice communication. We use our previously developed user-specific emotion recognition model to recognize user's emotion during communication and then to express it using an avatar to show to the partner. Another objective is to investigate the performance of our model in real-time environment using a stand-alone...
This study aims at developing an intelligent agent that can recognize user-specific emotions and can self-evolve. Previous studies have explored several methods to develop the model and improve the results while maintaining the feasibility of real-time implementation for later stages. We evolved the emotion recognition module by using Genetic Programming (GP) and explored several optimizations. We...
Customer experience depends not only on the aspects which retailers can easily control, but also on emotional factors that are unpredictable. In this paper, a Multi-Task MultiKernel learning approach is proposed to recognise positive users' emotion in a retail scenario. The overall system is composed by the Ultra-Wide Band (UWB) tracking system and a consumer smartwatch device. Data gathered from...
Snoring affects the sleep quality of the snorer itself and its social circle. Some types of snoring are related to sleep apnea, which leads to sleepiness during the day and to several health risks. Thus automatic detection of the different types of snoring may lead to more specific diagnosis and consequent treatment. In this work, we propose to use a reduced set of speech related features that includes...
The goal of this work is to validate the impact of natural elicitation of emotions by the speakers during the development of speech emotion databases for Malayalam language. The work also proposes a Gaussian Mixture Model-Deep Belief Networks (GMM-DBN) based speech emotion recognition system. To test the effect of emotion elicitation by the speakers, two independent datasets with emotionally biased...
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