Facial expression analysis for estimating patient´s emotional states in RPMS

Currently, a range of remote patient monitoring systems (RPMS) are being developed to care for patients at home rather than in the costly hospital environment. These systems allow remote monitoring by health professionals with minimum medical intervention to take place. However, they are still not as effective as one-on-one human interaction. The face and its features can convey patient cognitive and emotional states faster than electrical signals and facial expression can be considered as one of the most powerful features of RPMS. We present image pre-processing and enhancement techniques for face recognition applications. In particular, the project is aimed to improve the performance of RPMS, taking into account the cognitive and emotional state of patients by developing a more human like RPMS. The techniques use the value of grey scale of the images and extract efficient facial features. The extracted information is fed into input layer of an artificial neural network for face identification. On the other hand, the colour images are used by the recognition algorithm to eliminate nonskin coloured background and reduce further processing time. A data base of real images is used for testing the algorithms.