During a high-density EEG recording session, participants were shown film sequences analogous to those in Calbi et al. Face alignment is essential for better performance in facial expression recognition.
Results showed a significant effect of context on both valence and arousal in the fear condition only. Surprised faces were only posed with their mouths open.Materials and Methods Participants Twenty-four volunteers, without formal education in cinema, took part in the EEG and behavioural experiments: 11 female, 13 male, mean age The controversy surrounds the uncertainty about what specific emotional information is read from a facial expression. All of the photos were taken against the same off-white background with overhead lighting. Different from [ 21 , 35 , 46 ], Happy et al. The photographer was a trained research assistant with several years of experience working in a child development lab. However, previous experiments used mainly static images as stimuli or adopted experimental designs based on different non-POV versions of the Kuleshov experiment 37 , 38 , 41 , For each face, the participant was prompted to choose whether the face was sad, happy, surprised, angry, disgusted, fearful, or neutral. The stimuli included sequences in total, comprising 96 film sequences per emotional condition Neutral, Fear, or Happiness, in accordance with the emotion evoked by the Object shot. Open mouth disgusted faces included a tongue protrusion. Therefore, it is natural that research of facial emotion has been gaining lot of attention over the past decades with applications not only in the perceptual and cognitive sciences, but also in affective computing and computer animations [ 2 ]. This article has been cited by other articles in PMC.
Then, the dimensionality of the features is reduced to facilitate an efficient classification and enhance the generalization capability. We measured internal consistency reliability by calculating Cronbach's alpha scores between Time 1 and Time 2. Parents of the participating children signed a model release giving permission for the use of their photographs in research by the greater scientific community.
Third, the pre-trained FE classifiers, such as a support vector machine SVMAdaBoost, and random forest, produce the recognition results using the extracted features. LAURA source estimations for each solution point, normalized by root mean square, were then contrasted by means of paired t-tests.
LBP features were extracted from the 19 active patches and top 4 patches for classifying each pair of expressions were studied. There are two ways that facial expressions are generally validated in the literature.
Abstract Facial emotion recognition FER is an important topic in the fields of computer vision and artificial intelligence owing to its significant academic and commercial potential.