Smile intensity recognition in real time videos: fuzzy system approach

Facial emotion is a significant way of understanding or interpreting one’s inner thoughts. Real time video at any instant exhibits the emotion which serves as the input to the emotion recognition system. Many literatures propose different strategies in identifying the emotions by working on differen...

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Bibliographic Details
Published in:Multimedia tools and applications 2019-06, Vol.78 (11), p.15033-15052
Main Authors: Vinola, C, Vimala, Devi K
Format: Article
Language:English
Subjects:
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Decision making
Decision making models
Emotion recognition
Euclidean geometry
Fuzzy logic
Fuzzy systems
Multimedia Information Systems
Real time
Special Purpose and Application-Based Systems
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Summary:Facial emotion is a significant way of understanding or interpreting one’s inner thoughts. Real time video at any instant exhibits the emotion which serves as the input to the emotion recognition system. Many literatures propose different strategies in identifying the emotions by working on different features in the facial components, including geometrical, appearance and motion features. This paper considers the geometrical features as a prime component in deciding the intensity of the smile expressed in the real time videos of the AM-FED (Affectiva-MIT Facial Expression Dataset). Geometrical features considered in the work are the normalized Euclidean distance between the contributing LandMarkPoints (LMPs) of the eyes and the lip portions of the face. Fuzzy logic is applied to the system to effectively classify the intensity of the emotion, i.e., happiness or smile as Maximum, Moderate, Less and neutral. Being a Land mark based assessment, evaluating the normalized values of the Euclidean distance between LMPs for each frame of the video and then mapping the values of all the frames in a range helps the fuzzy decision making stage to relate the mapped values to the smile intensity of each frame. The average recognition rate obtained is 86.54%. The system contributes a less complex but nearly accurate smile intensity recognition model when compared to other computation intensive decision making models, with a practical significance in the customer or client’s mood/satisfactory identification in the online marketing and/or communication of the intensity of the smile of the conversing person to a visually challenged person.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-018-6890-8