Abstract
Technology nowadays is aiming to provide a better life quality for people, schools and universities are working for the convenient of the students as well as ensuring a high quality of education is attained. Emotions detections system can be a solution for better education results and may also be used as a part of human-computer interaction applications such as robotics, games, and intelligent tutoring system, This study shows potentials method of detecting emotions using mobile computing to recognize and identify emotions (Relax, Fear, Sadness, and Joy) based on facial skin temperature, more specifically 5 spots on the face, Nose, Glabellar line (between the eyes and eyebrows) right\lift cheeks and the chin, in addition to the Heart Rate Variability (HRV). An experiment was conducted with 20 healthy subjects (10 females and 10 males, 20 to 31 years old), Both visual and auditory media were used to induce these emotions in the experiment. By the end of this paper, the output data will be anglicized by an Artificial neural network (ANN) The Multilayer Perceptron (MLP) was selected as a classifier with a result of 88.75 % accuracy. This mechanism proves that human`s emotions can easily identify without physical interaction with the subject and with high reliability with only 0.11 misprediction rate
Highlights
In the last decade, [1,2,3,4,5,6,7,8,9,10,11,12] people have developed serval methods to detect and analyse emotions through different models such as facial expressions, walking gestures, and speech, these methods are tending to misjudgement in the emotions detection due to humans restraining form showing their true emotions in public, especially negative emotions and that goes under the social mask theory[1]
Another factor to misclassify these emotions is the using the average of the results as well as normalizing/standardizing methods may lead to high accuracy in the same time does not take the individual differences in concedes as every person is different from each other
Heart rate variability and facial skin temperature are on the top of the best parameters to detect emotions, facial skin temperature can be observed and analysis by using thermal camera, for observing the Heart Rate Variability (HRV), an HRV monitor was used to collect the HRV score which was calculated and provided by EIlet.HRV, Essence, human emotions can be identified using facial skin temperature and heart rate variability
Summary
In the last decade, [1,2,3,4,5,6,7,8,9,10,11,12] people have developed serval methods to detect and analyse emotions through different models such as facial expressions, walking gestures, and speech, these methods are tending to misjudgement in the emotions detection due to humans restraining form showing their true emotions in public, especially negative emotions and that goes under the social mask theory[1]. Heart rate variability and facial skin temperature are on the top of the best parameters to detect emotions, facial skin temperature can be observed and analysis by using thermal camera, for observing the HRV, an HRV monitor was used to collect the HRV score which was calculated and provided by EIlet.HRV, Essence, human emotions can be identified using facial skin temperature and heart rate variability
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