Sensor and Feature Level Fusion of Thermal Image and ECG Signals in Recognizing Human Emotions
C. M. Naveen Kumar1, G. Shivakumar2
1C. M. Naveen Kumar, Department of E & I Engineering, Malnad College of Engineering, Hassan affiliated to Visvesvaraya Technological University, Belagavi (Karnataka), India.
2G. Shivakumar, Department of E & I Engineering, Malnad College of Engineering, Hassan affiliated to Visvesvaraya Technological University, Belagavi (Karnataka), India.
Manuscript received on 03 December 2019 | Revised Manuscript received on 11 December 2019 | Manuscript Published on 31 December 2019 | PP: 78-82 | Volume-9 Issue-2S December 2019 | Retrieval Number: B10541292S19/2019©BEIESP | DOI: 10.35940/ijitee.B1054.1292S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Recent studies on recognition of various emotion labels concentrated on speech signals, text, visual images and anatomical variables. The proposed system combines the features of ECG which are extracted using empirical mode decomposition and features of thermal images which are extracted from Gray Level Co-occurrence Matrix (GLCM) viz energy, contrast, homogeneity and correlation. ECG is acquired from AD8232 module and thermal images from FLUKE TiS20. Data of ECG and thermal images are acquired simultaneously from a subject and database consists of data from 40 subjects in age group of 20 years to 40 years from Hassan, Karnataka, India. Here different labels of emotions have been classified based on K-nearest neighbor decision rule. This system yielded highest accuracy for disgust and lowest for anger using ECG and highest accuracy for disgust and surprise and least for sad.
Keywords: Human Emotions, Electrocardiogram, Thermal Image, Empirical Mode Decomposition.
Scope of the Article: Image Security