Facial-Expression Affective Attributes and their Configural Correlates: Components and Categories

The present study investigates the perception of facial expressions of emotion, and explores the relation between the configural properties of expressions and their subjective attribution. Stimuli were a male and a female series of morphed facial expressions, interpolated between prototypes of seven emotions (happiness, sadness, fear, anger, surprise and disgust, and neutral) from Ekman and Friesen (1976). Topographical properties of the stimuli were quantified using the Facial Expression Measurement (FACEM) scheme. Perceived dissimilarities between the emotional expressions were elicited using a sorting procedure and processed with multidimensional scaling. Four dimensions were retained in the reconstructed facial-expression space, with positive and negative expressions opposed along D1, while the other three dimensions were interpreted as affective attributes distinguishing clusters of expressions categorized as “Surprise-Fear,” “Anger,” and “Disgust.” Significant relationships were found between these affective attributes and objective facial measures of the stimuli. The findings support a componential explanatory scheme for expression processing, wherein each component of a facial stimulus conveys an affective value separable from its context, rather than a categorical-gestalt scheme. The findings further suggest that configural information is closely involved in the decoding of affective attributes of facial expressions. Configural measures are also suggested as a common ground for dimensional as well as categorical perception of emotional faces.Este estudio investiga la percepción de las expresiones faciales de la emoción y explora la relación entre las propiedades configurales de las expresiones y su atribución subjetiva. Los estímulos eran una serie de expresiones faciales transformadas por ordenador, interpuestas entre los prototipos de siete emociones (felicidad, tristeza, miedo, ira, sorpresa, asco y neutral) tomados de Ekman y Friesen (1976). Las propiedades topográficas de los estímulos se cuantificaron mediante el esquema Facial Expression Measurement (FACEM). Las disimilaridades percibidas entre las expresiones emocionales se elicitaron mediante un procedimiento de clasificación y se procesaron con escalonamiento multidimensional. Se retuvieron cuatro dimensiones en el espacio facial-expresión reconstruido, con expresiones positivas y negativas contrapuestas a lo largo de D1, y las restantes tres dimensiones se interpretaron como atributos afectivos, distinguiendo clusters de expresiones clasificadas como “Sorpresa/Miedo”, “Ira”, y “Asco”. Se hallaron relaciones significativas entre estos atributos afectivos y las medidas faciales objetivas de los estímulos. Los resultados apoyan un esquema explicativo componencial para el procesamiento de las expresiones, en el que cada componente de un estímulo facial conlleva un valor afectivo separable de su contexto, más que un esquema categórico de tipo Gestalt. Además sugieren que la información configural juega un papel importante en la decodificación de los atributos afectivos de las expresiones faciales Además, sugieren que las medidas configurales constituyen en terreno común de la percepción dimensional y categórica de las caras emocionales

Survey on Emotion Recognition Using Facial Expression

Automatic recognition of human affects has become more interesting and challenging problem in artificial intelligence, human-computer interaction and computer vision fields. Facial Expression (FE) is the one of the most significant features to recognize the emotion of human in daily human interaction. FE Recognition (FER) has received important interest from psychologists and computer scientists for the applications of health care assessment, human affect analysis, and human computer interaction. Human express their emotions in a number of ways including body gesture, word, vocal and facial expressions. Expression is the important channel to convey emotion information of different people because face can express mainly human emotion. This paper surveys the current research works related to facial expression recognition. The study attends to explored details of the facial datasets, feature extraction methods, the comparison results and futures studies of the facial emotion system