Human Feelings Identification using Facial Gesture
Anita Jindal1, Rashmi Priya2
1Anita Jindal*, Department of DEEE, G. D. Goenka University, Gurgaon, India.
2Dr. Rashmi Priya, Ph.D., Department of CSE, G. D. Goenka. University, Gurgaon, India.
Manuscript received on January 02, 2020. | Revised Manuscript received on January 15, 2020. | Manuscript published on January 30, 2020. | PP: 2168-2171 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6072018520/2020©BEIESP | DOI: 10.35940/ijrte.E6072.018520
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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: This paper describes the approach for a real-time facial gesture is used for human feelings identification, human feelings Identification (HFI) is an essential research fields in computer visions and artificial intelligence systems. Human Face is an import part of the body; it is used for non-verbal communication. This paper proposes a practical working model of human feelings detection on a single as well as group feelings identification, and Group feeling identification is a challenging problem due to obscuration of the hidden body pose variation, occlusion, variable lighting condition, indoor-outdoor siting, image quality. The group feeling identification are used in crowd analytics’, social media, marking, social event detection, public safety, human computing interaction and many more area. The proposed method consists of two-stage of detection: Face detection and Feelings identification. A Haar cascade method is used for detection of the input images and videos; the web camera is used to capture the real-time images and videos. This research is beneficial in the different area of applications Medical, Army, Navy, Airport and multiplex for security and virtual learning environments. Deep learning algorithm along with machine learning convolution neural network provides state of the art solution to classification, classification- localization, object detection, instance segmentation and image captioning. The significate percentage of seven human feelings identification rate in the form of accuracy is improving in emotions identification as compared to the previous schemes.
Keywords: Face Detection, Human Facial Feeling Identification, Deep Learning Algorithm, Convolutional Neural Networks.
Scope of the Article: Deep Learning.