In this paper, we propose a novel Bayesian approach to modelling temporal transitions of facial expressions represented in a manifold, with the aim of dynamical facial expression recognition in image sequences. A generalised expression manifold is derived by embedding image data into a low dimensional subspace using Supervised Locality Preserving Projections. A Bayesian temporal model is formul...

An efficient, local image-based approach for extraction of intransient facial features and recognition of four facial expressions from 2D image sequences is presented. The algorithm uses edge projection analysis for feature extraction and creates a dynamic spatio-temporal representation of the face, followed by classification through a feed-forward net with one hidden layer. A novel transform f...

Deep learning models have a wide number of applications including generating realistic-looking images. These typically require lots data, but we wanted to explore how much quality is sacrificed by using smaller amounts data. We built several and trained them at different dataset sizes, then assessed the generated images with widely used FID measure. As expected, measured an inverse correlation ...

In this work, we have explored several subspace reconstruction methods for facial ethnic appearance synthesis (FEAS). In our experiments, our proposed dual subspace modeling using the Fukunaga Koontz transform (FKT) yields much better facial ethnic synthesis results than the `1 minimization, the `2 minimization and the principal component analysis (PCA) reconstruction method. With that, we are ...

This paper presents a facial expression analysis system that performs recognition and emotional classification of human facial expression from a full-face image. The system consists of four main components. The first component performs face detection in an unstructured image using Artificial Neural Network. The second component is face recognition using Principal Component Analysis. The third c...