Online ISSN: 2515-8260

Keywords : Emotion Recognition


F. Ludyma Fernando, Dr. S. John Peter

European Journal of Molecular & Clinical Medicine, 2022, Volume 9, Issue 4, Pages 1960-1969

The affective quality called Valence refers to the intrinsic goodness (positive valence) or badness (negative valence) of an event, object, or situation. For this purpose, a model for classification and characterization of emotions have been developed which is discussed in this paper. In this model, the images are smoothened using an Average Filter and are first identified through a Convolutional Neural Network which uses the ReLU activation function. Then, the valence is classified using a Support Vector Machine (SVC) classifier, which uses a Radial Basis Function (RBF) kernel. For this reason, the emotions are labeled according to their nature. The positive emotions are labeled 1 (inclusive of the neutral emotion) and the negative emotions are labeled as 0. The images from the FER 2013 dataset is used for Valence Recognition and is given via a RBF Kernel in a SVM, which classifies whether the emotion recognized is positive or negative. The haarcascade algorithm is implemented to detect the face. In this paper, the 7 human emotions (happiness, surprise, fear, anger, fear, disgust, sadness and neutral) have been identified and their valence recognized.

Emotion Recognition Based on EEG using DEAP Dataset

Rama Chaudhary, Ram Avtar Jaswal, Sunil Dhingra

European Journal of Molecular & Clinical Medicine, 2021, Volume 8, Issue 3, Pages 3509-3517

Recognizing emotions at better accuracy is very challenging task. Therefore, in recent time, the human-machine interaction technology has gained so much success for recognizing the emotional states depending on physiological signals. The human emotional states can be detected by using facial expressions, but sometimes the accurate results are not achieved. Therefore in proposed work, the emotions are recognized using Electroencephalogram (EEG) which work on the basis of brain signal. Here, the human emotional states data is collected using DEAP Dataset and Artificial Neural Network (ANN) is used as classifier. Five time domain features namely correlation, average, variance, kurtosis and skewness are calculated for three frequency bands theta, alpha and beta. The data for two emotional dimensions valence and arousal is taken from DEAP Dataset. The proposed work gives better recognition results for valence and arousal dimensions which are 85.60 % and 87.36 % respectively. So we get the success in achieving significant accuracy.   

Review on Emotion Recognition using EEG Signals

Babu Chinta; Dr. Moorthi Madhavan

European Journal of Molecular & Clinical Medicine, 2021, Volume 8, Issue 2, Pages 986-992

In this paper, emotion recognition using EEG signals has been reviewed. The methods applied, dataset used for simulation, the results obtained along with the limitations and future work/gap is summarised in this review paper. This paves a way for the upcoming researchers to focus on the problems to be solved and the methods to be proposed as a novel new method or could be an integration or hybrid of the existing techniques or algorithms, along with the dataset to be used

An Emotion Recognition Based Fitness Application for Fitness Blenders

M. Suresh Anand; Nisha G Mathur; Angappan Kumaresan; J.K. Periasamy

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 5280-5288

The primary objective of the proposed work is to recognize emotions through speech using Recurrent-Neural-Networks and suggest physical exercises accordingly for the beneficiary of people who prefer to choose a balanced life style in an efficient way. Recurrent-Neural-Network (RNN) is a classifier technique which is utilized to classify various emotions which are happiness, disgust, sadness, fear, surprise and anger. Culmination of multiple features on similar databases are compared and clearly discussed. The outcome gives good result. At the outset the experimental results would shows that combining feature will yield higher accuracy rate on emotional databases using RNN classifier. Bayes Classifier, Hidden Markov Model (HMM), Support Vector Machine (SVM), Kernel deterioration and K Nearest Neighbors approach (KNN), Gaussian Mixture Model (GMM), Naïve-Bayes-classifier(NBC) were classifiers used by many researchers for human emotion recognition and translation. The main disadvantage is that only single input and corresponding output is given and combinations of output is a tough task and the algorithm gets costly. Our proposed system is used to suggest the physical exercise from speech. Energy, format, pitch, few spectrum features Mel Frequency Ceptral Coefficients (MFCC) and spectral Modulation features are the various common features extracted and used in modern research. In this work to extract emotional features the modulation spectral features used. To analyze, recognize and classify the emotions from audio speech samples the Standard English emotional database are used. This system will be helpful to people who need guidance in recognizing their emotion and to get a better physical exercise suggestion.