Abstract
Human machine interaction is one of the emerging fields for the coming years. Interacting with others in our daily life is a face to face interaction. Faces are the natural way of interaction between humans and hence also useful in human machine interaction.
This paper describes a novel technique to recognize the human facial expressions and manipulating this task for human machine interaction. We use 2D model based approach for human facial expression recognition. An active shape model (ASM) is fitted to the face image and texture information is extraced. This shape and texture information is combined with optical flow based temporal information of the image sequences to form a feature vector for the image. We experimented on image sequences of 97 different persons of Cohn-Kanade-Facial Expression Database. A classification rate of 92.4% is obtained using a binary decision tree classifier, whereas a classification rate of 96.4% is obtained using pairwise classifier based on support vector machines. This system is capable to work in realtime.
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Riaz, Z., Mayer, C., Beetz, M., Radig, B. (2009). Facial Expressions Recognition from Image Sequences. In: Esposito, A., Vích, R. (eds) Cross-Modal Analysis of Speech, Gestures, Gaze and Facial Expressions. Lecture Notes in Computer Science(), vol 5641. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03320-9_29
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DOI: https://doi.org/10.1007/978-3-642-03320-9_29
Publisher Name: Springer, Berlin, Heidelberg
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