Facial Emotion Recognition for Human Computer Interaction Using a Fuzzy Model in the E-Business

In this paper we present a facial expression recognition model using fuzzy techniques in order to further detect human behaviors in the e-business. In this model, fuzzy clustering model is proposed to classify images, after extract the features that are used as inputs into a classification system. The outcome of the model is one of the preselected emotion categories. The motivation for the model is to deal with the inconsistency between human perception and the machine recognition. For this purpose, we use the Fuzzy c-Means (FCM) to partition a given data set into homogeneous clusters for interpreting the emotion of a face image; by homogeneous we mean that all points in the same cluster share similar attributes and they do not share similar attributes with points in other clusters. Since every clustering process is uncertain in nature, fuzzy classification methods are initially applied in this field. Regarding conventional crisp sets, the concepts of fuzzy sets provides more robust representations of the model of real word objects. The proposed model is based on the theoretical foundations and FCM empirical results in which it is presented the usefulness and effectiveness of the clustering algorithm by partitioning different real data sets.