Facial Expression Recognition Based on Image Feature
Facial expressions provide an important behavioral measure for the study of emotions, cognitive processes and social interaction and thus automatic facial expression recognition systems can provide a less intrusive method to apprehend the emotion activity of a person of interest. With the availability of low cost imaging and computational devices, automatic facial recognition systems now have a potential to be useful in several day-to-day application environments like in identifying suspicious persons in airports, railway stations and other places with higher threat of terrorism attacks.
Most transformer failures are attributed to bushings failures. Hence it is necessary to monitor the condition of bushings. In this paper three methods are developed to monitor the condition of oil filled bushing. Multi-layer perceptron (MLP), Radial basis function (RBF) and Rough Set (RS) models are developed and combined through majority voting to form a committee. The MLP performs better that the RBF and the RS is terms of classification accuracy. The RBF is the fasted to train. The committee performs better than the individual models. The diversity of models is measured to evaluate their similarity when used in the committee.