A Prediction of Emotions for Recognition of Facial Expressions Using Deep Learning
K. Sravanthi1, G. Jaya Suma2
1K. Sravanthi, Assistant Professor, Dr. Br. Ambedkar University, Etcherla, Srikakulam, (Andhra Pradesh), India.
2Dr. G. Jaya Suma, HOD, Information Technology, Jntuk, Vizianagaram (Andhra Pradesh), India.
Manuscript received on 13 October 2019 | Revised Manuscript received on 22 October 2019 | Manuscript Published on 02 November 2019 | PP: 1076-1079 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B11830982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1183.0982S1119
Open Access | Editorial and Publishing 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: Automated facial expression recognition can greatly improve the human–machine interface. Many deep learning approaches have been applied in recent years due to their outstanding recognition accuracy after training with large amounts of data. In this research, we enhanced Convolutional Neural Network method to recognize 6 basic emotions and compared some pre processing methods to show the influences of its in CNN performance. The preprocessing methods are :resizing, mean, normalization, standard deviation, scaling and edge detection . Face detection as single pre-processing phase achieved significant result with 100 % of accuracy, compared with another pre-processing phase and raw data.
Keywords: Emotion Recognition; Convolutional Neural Network, Feature Extraction, Facial Action Coding System.
Scope of the Article: Deep Learning