Abstract
This research uses an innovative way to detect and recognize user’s emotion with RGB color system. The RGB color system that is widely used in diverse computer system is an additive color model in which red, green, and blue light are added together showing various color. This study was based on Thayer’s emotion model which describes the emotion with two vectors, valence and arousal, and gathers the emotion color with RGB as input of neural network for calculating and forecasting user’s emotion. In this experiment, using 320 data translate to quarter into emotion groups to train the weight in the neural network and uses 160 data to proof the accuracy. The result reveals that this model can be valid estimated the emotion by reply color response from examinee. In other hand, this experiment found that trend of the different element of color on Cartesian coordinate system figures out the distinguishing intensity in RGB color system. Via the foregoing detect emotion model is going to design an affective computing intelligence framework try to embed the emotion component in it.
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Lee, MF., Chen, GS., Wang, JC. (2014). Using Affective Computing to Detect Emotions with Color. In: Park, J., Zomaya, A., Jeong, HY., Obaidat, M. (eds) Frontier and Innovation in Future Computing and Communications. Lecture Notes in Electrical Engineering, vol 301. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8798-7_41
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DOI: https://doi.org/10.1007/978-94-017-8798-7_41
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