Facial expression recognition based on compressive sensing and pyramid processing

In this paper, a new approach has been proposed for improved facial expression recognition. the new approach is inspired by the compressive sensing theory and multi-resolution approach to facial expression problems. initially, each image sample is decomposed into desired levels of its pyramids at different sizes and resolutions. at each level of the pyramid, features are extracted using a measurement matrix based on compressive sensing theory. these measurements are concatenated together to form a feature vector for the original image. the results obtained from the approach using three distance measurement classifiers (manhattan, euclidean, cosine) and support vector machine are impressive and outperforms most of its counterpart algorithms in the literature using the same databases and settings.