Evaluating the Emotional State of a User Using a Webcam

Abstract

-In online learning is more difficult for teachers identify to see how individual students behave. Student's emotions like self-esteem, motivation, commitment, and others that are believed to be determinant in student's performance can not be ignored, as they are known (affective states and also learning styles) to greatly influence student's learning. The ability of the computer to evaluate the emotional state of the user is getting bigger attention. By evaluating the emotional state, there is an attempt to overcome the barrier between man and non-emotional machine. Recognition of a real time emotion in e-learning by using webcams is research area in the last decade. Improving learning through webcams and microphones offers relevant feedback based upon learner's facial expressions and verbalizations. The majority of current software does not work in real time -scans face and progressively evaluates its features. The designed software works by the use neural networks in real time which enable to apply the software into various fields of our lives and thus actively influence its quality. Validation of face emotion recognition software was annotated by using various experts. These expert findings were contrasted with the software results. An overall accuracy of our software based on the requested emotions and the recognized emotions is 78%. Online evaluation of emotions is an appropriate technology for enhancing the quality and efficacy of e-learning by including the learner´s emotional states.

A fuzzy logic approach to reliable real-time recognition of facial emotions

“…Notwithstanding the limitations of emotion recognition described above, topical hardware developments on regular computer equipment [23] would now enable emotion recognition at a larger scale [7]. A typical example would be the use of common webcams for emotion recognition from facial expressions [7,52]. It has been suggested that e-learning applications can benefit from such emotion recognition devices for more natural interactions [71] because they collect data of learners continuously and unobtrusively [7,8].…”

Motion Detection and Face Recognition using Raspberry Pi, as a Part of, the Internet of Things

“…Real-time detection of the face with many other objects in the background is not a simple problem. There are situations in which it is not possible to capture the face of an object due to face rotation of more than about 30°, or to a change in the lighting, other factors which might inhibit facial appearance, such as, beards, glasses [18] [19]. The second phase is extraction, and this is determined by certain specifications and requires particular features to detect an object.…”

Використання Виразів Емоцій На Обличчі Людини В Системах Електронного Навчання

“…Recently, real-time emotion recognition studies have been encountered in e-learning. For instance, Magdin et al [14] have designed software, which evaluates the emotional state of the user using a webcam. The system works in real time using neural networks.…”