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Automatic emotion analysis and understanding has received much attention over the years in affective computing. Recently, there are increasing interests in inferring the emotional intensity of a group of people. For group emotional intensity analysis, feature extraction and group expression model are two critical issues. In this paper, we propose a new method to estimate the happiness intensity of a group of people in an image. Firstly, we combine the Riesz transform and the local binary pattern descriptor, named Riesz-based volume local binary pattern, which considers neighbouring changes not only in the spatial domain of a face but also along the different Riesz faces. Secondly, we exploit the continuous conditional random fields for constructing a new group expression model, which considers global and local attributes. Intensive experiments are performed on three challenging facial expression databases to evaluate the novel feature. Furthermore, experiments are conducted on the HAPPEI database to evaluate the new group expression model with the new feature. Our experimental results demonstrate the promising performance for group happiness intensity analysis.

We present a novel approach to pose-invariant face recognition that handles continuous pose variations, is not database-specific, and achieves high accuracy without any manual intervention. Our method uses multidimensional Gaussian process regression to learn a nonlinear mapping function from the 2D shapes of faces at any non-frontal pose to the corresponding 2D frontal face shapes. We use this mapping to take an input image of a new face at an arbitrary pose and pose-normalize it, generating a synthetic frontal image of the face that is then used for recognition. Our fully automatic system for face recognition includes automatic methods for extracting 2D facial feature points and accurately estimating 3D head pose, and this information is used as input to the 2D pose-normalization algorithm. The current system can handle pose variation up to 45 degrees to the left or right (yaw angle) and up to 30 degrees up or down (pitch angle). The system demonstrates high accuracy in recognition experiments on the CMU-PIE, USF 3D, and Multi-PIE databases, showing excellent generalization across databases and convincingly outperforming other automatic methods.

Face recognition in real-world conditions requires the ability to deal with a number of conditions, such as variations in pose, illumination and expression. In this paper, we focus on variations in head pose and use a computationally efficient regression-based approach for synthesising face images in different poses, which are used to extend the face recognition training set. In this data-driven approach, the correspondences between facial landmark points in frontal and non-frontal views are learnt offline from manually annotated training data via Gaussian Process Regression. We then use this learner to synthesise non-frontal face images from any unseen frontal image. To demonstrate the utility of this approach, two frontal face recognition systems (the commonly used PCA and the recent Multi-Region Histograms) are augmented with synthesised non-frontal views for each person. This synthesis and augmentation approach is experimentally validated on the FERET dataset, showing a considerable improvement in recognition rates for ±40◦ and ±60◦ views, while maintaining high recognition rates for ±15◦ and ±25◦ views.