Electromagnetic based emotion recognition using ANOVA feature selection and Bayes Network

The paper discusses the development of emotion recognition system which can be applied to a wider range of human population. This is achieved by measuring the unique electromagnetic (EM) signal generated upon invoking certain emotions. A set of audio-visual stimulants is designed to invoke the desired emotions under study that are happy, sad and nervous. A set of questionnaire is developed to verify the stimulant effectiveness in invoking the emotion. The recognition of the emotion is deduced from the measured electromagnetic signals radiated from the human body by a handheld device called Resonant Field Imaging (RFI™). There are ten points of interest (POIs) on the body where the signals are measured to form the dataset which later fed into Bayes Network (BN) to classify the emotion. ANOVA test is run in selecting the best features to classify the emotions. The result after eliminating 6 from 10 POIs demonstrates the system performance is not compromised. The efficiency of ANOVA and BN in selecting the best features to model the emotion recognition system has successfully optimized the cost of the system and reduced the time to measure the signals quite significantly.