Deep Learning-based Facial Expression Recognition and Analysis for Filipino Gamers
Juan Raphael Sena1, Melvin Cabatuan2
1Juan Raphael Sena, Department of Electronics and Communications Engineering, De La Salle University, Manila, Philippines.
2Melvin Cabatuan, Department of Electronics and Communications Engineering, De La Salle University, Manila, Philippines.
Manuscript received on 02 March 2019 | Revised Manuscript received on 06 March 2019 | Manuscript published on 30 July 2019 | PP: 1822-1827 | Volume-8 Issue-2, July 2019 | Retrieval Number: B1027078219/19©BEIESP | DOI: 10.35940/ijrte.B1027.078219
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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: This paper presents a computer vision based emotion recognition system for the identification of six basic emotions among Filipino Gamers using deep learning techniques. In particular, the proposed system utilized deep learning through the Inception Network and Long-Short Term Memory (LSTM). The researchers gathered a database for Filipino Facial Expressions consisting of 74 gamers for the training data and 4 gamer subjects for the testing data. The system was able to produce a maximum categorical validation accuracy of .9983 and a test accuracy of .9940 for the six basic emotions using the Filipino database. The cross-database analysis results using the well-known Cohn -Kanade+ database showed that the proposed Inception-LSTM system has accuracy on a par with the current existing systems. The results demonstrated the feasibility of the proposed system and showed sample computations of empathy and engagement based on the six basic emotions as a proof of concept.
Index Terms: Deep Learning, Gamer Facial Expression, Emotion Recognition, Inception Network, LSTM.
Scope of the Article: Deep Learning