Mood Sensing using Facial Landmarks 
Kuppala Sushmanth Sai1, Yaramala Srinath2, P.Sabitha3, Bejawada Venkatesh4, Raghavendran R5
1Kuppala Sushmanth Sai, Department of Computer Science, SRM University Ramapuram, Chennai (Tamil Nadu), India.
2Yaramala Srinath, Department of Computer Science, SRM University Ramapuram, Chennai (Tamil Nadu), India.
3P. Sabitha, M.E, Assistant Professor, Department of Computer Science and Engineering, SRM Institute of Science and Technology Chennai (Tamil Nadu), India.
4Bejawada Venkatesh, Department of Computer Science, SRM University Ramapuram, Chennai (Tamil Nadu), India.
5Raghavendran, Department of Computer Science, SRM University Ramapuram, Chennai (Tamil Nadu), India.
Manuscript received on 25 August 2019 | Revised Manuscript received on 01 September 2019 | Manuscript Published on 14 September 2019 | PP: 1-4 | Volume-8 Issue-5S3, July 2019 | Retrieval Number: E10020785S319/19©BEIESP | DOI: 10.35940/ijeat.E1002.0785S319
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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: Communication plays a pivotal role in every person’s life. There are various types of communications in which some are verbal and some are non-verbal. Expressions on a person’s face are a type of non-verbal communication. Expressions on the face can be used to define how the person is feeling, recognizing them helps to enhance the human-machine interaction. Thus we propose a system that is un-affected by the illumination changes or the light changes. Expressions on the human face can be computed by using CLM, constrained local models inserts a dense model to a new input image to get the emotions stats .SVM classifier is used to distinguish the input image into different emotion categories. Results showed a remarkable increase in efficiency and performance. Change in lighting conditions will have a very little effect on the efficiency of the system.
Keywords: SVM, MEM, CLM.
Scope of the Article: Remote Sensing