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Personal emotions accompany us in our daily life, affecting our learning and work, therefore it is necessary to obtain better understanding of human behavior through emotional assessment. This paper proposes a method for recognizing emotions electroencephalography(EEG) based on relevance vector machine(RVM). Emotional states of two types as positive and negative were selected from a standard database...
This paper presents a method for emotion recognition by using LLE - Largest Lyapunov exponent of the Mel-frequency energy bands for the Romanian language. The emotion recognition for features vectors that contains LLE is better using Support Vector Machine - SVM classifier (76.4%) than Weighted K-Nearest Neighbors - WKNN classifier (72.8%). The most efficient combination was LLE with LPC - linear...
Emotions constantly guide and modulate our rationality which plays an essential role in how we behave intelligently while interacting with other humans as well as machines. The technique described here provides an effective interface between humans and machines using facial expressions. This technique could be used to allow machines to incorporate an interpretation of human emotions in their principles...
Since the inception of the concept of social networking, communication patterns have shifted drastically with the unmitigated trend in socializing over the Internet, especially when people began connecting via mobile devices. Nowadays people tend to use these modern communication systems to share their emotions with each other. Human emotions play a vital role in human relationships and people share...
In this paper we analysis the speech emotions related to cognitive process. An automatic system is established for detecting speech emotions including anxiety, hesitation, confidence and joy. In order to obtain a naturalistic database we use noise to induce negative emotions, sleep deprivation is also used for this purpose. The lack of sleep is an important cause for anxiety. Annotation of emotional...
There are some problems to be resolved for speech emotion recognition, such as the dimension of feature sets is usually too high and the redundancy among various features is relatively stronger. Considering these problems, the factor analysis and majority voting based speech emotion recognition was proposed. How to extract emotional factors from global statistical features and GMM super vectors was...
The classification of emotions, such as joy, anger, anxiety, etc. from tonal variations in human speech is an important task for research and applications in human computer interaction. In the preceding work, it has been demonstrated that the locally extracted features of speech match or surpass the performance of global features that has been adopted in current approaches. In this continuing research,...
Emotional state recognition is an important component for efficient human-computer interaction. Most existing works address this problem using 2D features, but they are sensitive to head pose, clutter, and variations in lighting conditions. The general 3D based methods only consider geometric information for feature extraction. In this paper, we present a real 3D visual features based method for human...
Facial expressions play a critical role in human-human communications. There is currently a great need in automatic facial expression classification technology. To investigate this issue, we proposed a new smile classification method for real-world conditions. It demonstrated that the PHOG features achieve as high a classification rate as the traditional Gabor features did with lower feature dimensions...
The paper provides a novel approach to emotion recognition from facial expression and voice of subjects. The subjects are asked to manifest their emotional exposure in both facial expression and voice, while uttering a given sentence. Facial features including mouth-opening, eye-opening, eyebrow-constriction, and voice features including, first three formants: F1, F2, and F3, and respective powers...
This paper presents a novel facial expression recognition methodology. In order to classify the expression of a test face to one of seven pre-determined facial expression classes, multiple two-class classification tasks are carried out. For each such task, a unique set of features is identified that is enhanced, in terms of its ability to help produce a proper separation between the two specific classes...
EEG-based emotion recognition is a relatively new research field in the human computer interaction area and its aim is the implementation of new algorithms that would identify and recognize emotions from EEG (electroencephalogram) signals. Towards that, a novel method is presented in this paper that employs an optimized hybrid filter, using empirical mode decomposition (EMD) and genetic algorithms...
Emotions that are elicited in response to a video scene contain valuable information for multimedia tagging and indexing. The novelty of this paper is to introduce a Bayesian classification framework for affective video tagging that allows taking contextual information into account. A set of 21 full length movies was first segmented and informative content-based features were extracted from each shot...
In this paper, a novel geometric features extraction method for facial expression recognition is proposed. ASM automatic fiducial point location algorithm is firstly applied to a facial expression image, and then calculating the Euclidean distances between the center of gravity coordinate and the annotated fiducial points' coordinates of the face image. In order to extract the discriminate deformable...
Modeling time series data of varying length is important in different domains. There are two paradigms for modeling the varying length sequential data. Tasks such as speech recognition need modeling the temporal dynamics and the correlations among the features. Hidden Markov models (HMM) are used for these tasks. In tasks such as speaker recognition, audio classification and speech emotion recognition,...
Emotion recognition from electrocardiography (ECG) signal has become an important research topic in the field of affective computing. In the current work, ECG signals from multiple subjects were collected when film clips shown to them, and then feature sets were extracted from precise location of P-QRS-T wave by continuous wavelet transform (CWT). Hybrid particle swarm optimization (HPSO) was utilized...
This paper proposes a novel method for facial expression recognition by using independent component analysis of Gabor features. In the feature extraction stage, Gabor feature vectors are firstly extracted from a set of facial expressions images, then using independent component analysis (ICA) to extract the independent Gabor features. After that, the independent Gabor features are used to train SVM...
Speaker independent emotion recognition is particularly difficult for the individual differences of acoustic character and culture background. So, relative features obtained by calculating the features change of emotion speech relative to natural speech are adopted to weaken the influence from the individual differences in the paper. Moreover, an improved ranked voting fusion system is proposed to...
Recent developments in the field of facial expression recognition advocate the use of feature vectors based on local binary patterns (LBP). Research on the algorithmic side addresses robustness issues when dealing with non-ideal illumination conditions. In this paper, we address the challenges related to mapping these algorithms on smart camera platforms. Algorithmic partitioning taking into account...
In order to overcome slow speed of traditional PCA, the paper presents that feature vector can be obtained by feature block two dimensional principal component analysis, and the Manhattan distance classifier output recognition results. Calculation speed can be enhanced efficiently. Compared with Euclidean distance, recognition rate is improved by Manhattan distance. The experiments of training data...
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