Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/449548
Title: | Emotion Recognition for HRI Applications |
Researcher: | Saxena, Suchitra |
Guide(s): | Tripathi, Shikha |
Keywords: | Engineering Engineering and Technology Engineering Electrical and Electronic |
University: | PES University |
Completed Date: | 2022 |
Abstract: | Emotion is an of state mind resulting in strong feelings derived from one s mood, circumstances or relationship with others. Emotions positively affect intelligent functions such as decision making, coping, perception, planning, cognition, reasoning and empathic understanding. Since emotions play an important role in day-to-day life, emotion recognition comes as an interesting research field that finds immense applications. Emotion recognition can be classified into two major categories. One that uses physical signals like text, speech, gestures, facial expression and the other category that uses internal newlinesignals, like the physiological signals. In 1968, Albert Mehrabian proved that in human-to human interaction 7% of communication is contributed by verbal cues, 38% is contributed by vocal cues and the major portion of 55% is contributed by facial expressions [Mehrabian,1968]. It shows facial expression analysis as one of the most important components for emotion recognition. newlineFacial expression analysis for single or multiple faces in real time has multiple applications in daily life and makes life easier. Facial analysis of multiple faces can be used in surveillance for crowd monitoring for security, control and management purposes. Another security layer can be added to security at malls, airports, sports arenas, and other public venues to detect malicious intent. It can be used in classrooms or in automated tutors/online education to spot struggling students, or help autistics better interact with others. Emotion recognition can be used at hospitals for monitoring patient conditions in ICUs to check if the patient is in pain or fear. newlineRecognizing emotions from facial expressions using images that are varying in pose, illumination and age at real time is a challenging task. The most expressive features on the face are eye, eyebrow, nose, chin and mouth regions. A suitable quotfeature-classifierquot combination improves the accuracy of emotion recognition. |
Pagination: | xxiv, 151 |
URI: | http://hdl.handle.net/10603/449548 |
Appears in Departments: | Department of Electronics and Communication Engineering |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
01_title.pdf | Attached File | 86.41 kB | Adobe PDF | View/Open |
02_prelim pages.pdf | 1.01 MB | Adobe PDF | View/Open | |
03_content.pdf | 16.4 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 94.11 kB | Adobe PDF | View/Open | |
05_chapter 1.pdf | 307.5 kB | Adobe PDF | View/Open | |
06_chapter 2.pdf | 378.7 kB | Adobe PDF | View/Open | |
07_chapter 3.pdf | 1.37 MB | Adobe PDF | View/Open | |
08_chapter 4.pdf | 757.17 kB | Adobe PDF | View/Open | |
09_chapter 5.pdf | 2.06 MB | Adobe PDF | View/Open | |
10_chapter 6.pdf | 1.17 MB | Adobe PDF | View/Open | |
11_chapter 7.pdf | 100.3 kB | Adobe PDF | View/Open | |
12_annexures.pdf | 223.73 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 190.6 kB | Adobe PDF | View/Open |
Items in Shodhganga are licensed under Creative Commons Licence Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).
Altmetric Badge: