“…More specifically, same features is impossible to be extracted from same person image within particular amount of time. Aging [5], is one of the most challenges in image recognition system (IRS). Other disturbances alike bad illumination [6], face wear and face orientation [7], noise due to dusts [8] are the common degradations of ERS performance.…”
Section: Rescores and Structurementioning
confidence: 99%
“…Brain activity is one of outstanding approaches that can be utilized in various applications including emotional detection. EEG data is used for this purpose in many previous researches such as [4] and [5]. this data can be recorded using set of electrodes that to be placed over the skull.…”
Section: Rescores and Structurementioning
confidence: 99%
“…The invention of a virtual education portal for knowledge transmission was spurred on, however, by other issues such as financial crises when students cannot afford the enrollment fees or pandemic scenarios where educational organizations themselves are not operating [4]. Furthermore, educational enhancement experience is also supported in [5] and [6] by incorporating of graphical objects e.g. avatars form motivating students/learners to enroll in education.…”
Educational applications of image processing have emerged due to data collection tools development. Education is vital field in human life where highly accurate performance is required. Integrating image processing and deep learning with the education will help to optimize the performance of entire system. It is possible now to make out the student’s emotional status through study the features from facial images taken for a group of students. That reduces the time and cost of the education by providing a facility similar to the regular classrooms environments. Which may help plenty of people who are unable to access regular educational facilities due to intolerable cost. In this paper, automatic emotional detection is being performed using neural network. Two models are used namely artificial neural network and CNN neural network. The models are tested using emotional images data. Results are reported 96.7 % and 99.2 % accuracies from bother artificial neural network and CNN respectively.
“…More specifically, same features is impossible to be extracted from same person image within particular amount of time. Aging [5], is one of the most challenges in image recognition system (IRS). Other disturbances alike bad illumination [6], face wear and face orientation [7], noise due to dusts [8] are the common degradations of ERS performance.…”
Section: Rescores and Structurementioning
confidence: 99%
“…Brain activity is one of outstanding approaches that can be utilized in various applications including emotional detection. EEG data is used for this purpose in many previous researches such as [4] and [5]. this data can be recorded using set of electrodes that to be placed over the skull.…”
Section: Rescores and Structurementioning
confidence: 99%
“…The invention of a virtual education portal for knowledge transmission was spurred on, however, by other issues such as financial crises when students cannot afford the enrollment fees or pandemic scenarios where educational organizations themselves are not operating [4]. Furthermore, educational enhancement experience is also supported in [5] and [6] by incorporating of graphical objects e.g. avatars form motivating students/learners to enroll in education.…”
Educational applications of image processing have emerged due to data collection tools development. Education is vital field in human life where highly accurate performance is required. Integrating image processing and deep learning with the education will help to optimize the performance of entire system. It is possible now to make out the student’s emotional status through study the features from facial images taken for a group of students. That reduces the time and cost of the education by providing a facility similar to the regular classrooms environments. Which may help plenty of people who are unable to access regular educational facilities due to intolerable cost. In this paper, automatic emotional detection is being performed using neural network. Two models are used namely artificial neural network and CNN neural network. The models are tested using emotional images data. Results are reported 96.7 % and 99.2 % accuracies from bother artificial neural network and CNN respectively.
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