RETRACTED ARTICLE: A modified fuzzy histogram of optical flow for emotion classification

Human beings tend to express various emotions based on the activities. The criticality of facial expression has been recognized widely in the social interaction along with the social intellect. Human perception is subjective in nature and this makes the classification of emotion an extremely challenging problem. The mood, personality, age, and environment have a major influence on the perception of emotion. Facial expressions are a key to emotion; various studies devote on emotion classification based on facial expression. For the identification of these emotions, there is a mixture of models that make use of the feature representation that are gradient based, a mixture of various dynamic textures along with contextual information. In this work, histogram of optical flow (HOF) was used for the extraction of features and a neural network for bringing about an improvement to the accuracy of classification. With the availability of big data analytics, there has been a major increase in the power of computation in terms of analysing live video data, huge number of images and faster processing which is critical for emotion classification. The work has investigated efficacy of the flow of HOF and proposed a modified fuzzy histogram of optical flow. For choosing optimal rules in fuzzy system, heuristic method namely, charged system search was used. The results have proved that there has been a significant improvement to the methodology proposed.