The Indian Spontaneous Expression Database for Emotion Recognition (original) (raw)
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The advent of artificial intelligence technology has reduced the gap between humans and machines as equips man to create more near-perfect humanoids. Facial expression is an important tool to communicate one’s emotions as a non-verbally overview of emotion recognition using facial expressions. A remarkable advantage of such a technique recently improved public security through tracking and recognizing, thus led to the high attention to keep up the scientific research in the field. The approaches used for facial expression include classifiers like Support Vector Machine (SVM), Artificial Neural Network (ANN), Convolution Neural Network (CNN), Active Appearance and Machine learning which all used to classify emotions based on certain parts of interest on the face like lips, lower jaw, eyebrows, cheeks and many more. By comparison, the reviews have shown that the average accuracy of the basic emotion ranged from 51% up to 100%, whereas carrying through 7% to 13% in the compound emotion...
BNU-LSVED：a multimodal spontaneous expression database in educational environment
In the field of pedagogy or educational psychology, emotions are treated as very important factors, which are closely associated with cognitive processes. Hence, it is meaningful for teachers to analyze students' emotions in classrooms, thus adjusting their teaching activities and improving students ' individual development. To provide a benchmark for different expression recognition algorithms, a large collection of training and test data in classroom environment has become an acute problem that needs to be resolved. In this paper, we present a multimodal spontaneous database in real learning environment. To collect the data, students watched seven kinds of teaching videos and were simultaneously filmed by a camera. Trained coders made one of the five learning expression labels for each image sequence extracted from the captured videos. This subset consists of 1572 multimodal spontaneous expression image sequences recorded in real classrooms. There are four main advantages in this database. 1) Due to recorded in the real classroom environment, viewer's distance from the camera and the lighting of the database varies considerably between image sequences. 2) All the data presented are natural spontaneous responses to teaching videos. 3) The multimodal database also contains nonverbal behavior including eye movement, head posture and gestures to infer a student's affective state during the courses. 4) In the video sequences, there are different kinds of temporal activation patterns. In addition, we have demonstrated the labels for the image sequences are in high reliability through Cronbach's alpha method.
International Journal of INTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING, 2024
Facial expression recognition is a crucial area of study in the field of computer vision. Research on nonverbal communication has shown that a significant amount of deliberate information is sent via facial expressions. Facial expression recognition is a crucial field in computer vision that deals with the significant impact of nonverbal communication. Expression recognition has lately been extensively used in the medical and advertising sectors. Difficulties in Facial Emotion Recognition. Facial emotion recognition is a technique that examines facial expressions in static photos and videos to uncover information about an individual's emotional state. The intricacy of facial expressions, the versatile use of the technology in any setting, and the incorporation of emerging technologies like artificial intelligence pose substantial privacy hazards. Facial expressions serve as non-verbal cues, offering indications of human emotions. Deciphering emotional expressions has been a focal point of study in psychology for many years. This study will examine several prior studies that have undertaken comprehensive facial analysis, including both total and partial face recognition, to identify expressions and emotions. The datasets and models used in previous studies, as well as the findings gained, show that employing the whole face yields more accuracy compared to using specific face-parts, which result in lower accuracy ratios. However, emotional identification often does not rely only on the whole face, since it is not always feasible to have the full face available. Contemporary research is now prioritising the identification of facial expressions based on certain facial features. Efficient deep learning algorithms, particularly the CNN algorithm, can do this task.
Human Facial Expression Recognition using Machine learning Algorithms | IJSRDV6I90230
IJSRD - International Journal for Scientific Research and Development, 2018
— The human facial expressions play an important role in recognizing one's intention or mood of that respective person. Facial expressions are the changes that occur on the face based on the internal emotions of the person. This paper focuses on using black and white images for the recognition of the facial expression and also identify the emotions involved in the expression. Here Machine Learning technique is used to train the system to understand one's facial expressions which will in turn help it to judge the state of mind of the person and provide appropriate user experience.