Facial Asymmetry Analysis Based on 3-D Dynamic Scans

Facial dysfunction is a fundamental symptom which often relates to many neurological illnesses, such as stroke, Bell’s palsy, Parkinson’s disease, etc. The current methods for detecting and assessing facial dysfunctions mainly rely on the trained practitioners which have significant limitations as they are often subjective. This paper presents a computer-based methodology of facial asymmetry analysis which aims for automatically detecting facial dysfunctions. The method is based on dynamic 3-D scans of human faces. The preliminary evaluation results testing on facial sequences from Hi4D-ADSIP database suggest that the proposed method is able to assist in the quantification and diagnosis of facial dysfunctions for neurological patients

Is 2D Unlabeled Data Adequate for Recognizing Facial Expressions?

Automatic facial expression recognition is one of the important challenges for computer vision and machine learning. Despite the fact that many successes have been achieved in the recent years, several important but unresolved problems still remain. This paper describes a facial expression recognition system based on the random forest technique. Contrary to the many previous methods, the proposed system uses only very simple landmark features, with the view of a possible real-time implementation on low-cost portable devices. Both supervised and unsupervised variants of the method are presented. However, the main objective of the paper is to provide some quantitative experimental evidence behind more fundamental questions in facial articulation analysis, namely the relative significance of 3D information as oppose to 2D data only and importance of the labelled training data in the supervised learning as opposed to the unsupervised learning. The comprehensive experiments are performed on the BU-3DFE facial expression database. These experiments not only show the effectiveness of the described methods but also demonstrate that the common assumptions about facial expression recognition are debatable

Is the 2D unlabelled data adequate for facial expressionrecognition?

Automatic facial expression recognition is one of the important challenges for computer vision and machine learning. Despite the fact that many successes have been achieved in the recent years, several important but unresolved problems still remain. This paper describes a facial expression recognition system based on the random forest technique. Contrary to the many previous methods, the proposed system uses only very simple landmark features, with the view of a possible real-time implementation on low-cost portable devices. Both supervised and unsupervised variants of the method are presented. However, the main objective of the paper is toprovide some quantitative experimental evidence behind more fundamental questions in facial articulation analysis, namely the relative significance of 3D information as oppose to 2D data only and importance of the labelled training data in the supervised learning as opposed to the unsupervised learning. The comprehensive experiments are performed on the BU-3DFE facial expression database. These experiments not only show theeffectiveness of the described methods but also demonstrate that the common assumptions about facial expression recognition are debatable

Recognising facial expressions in video sequences

We introduce a system that processes a sequence of images of a front-facing human face and recognises a set of facial expressions. We use an efficient appearance-based face tracker to locate the face in the image sequence and estimate the deformation of its non-rigid components. The tracker works in real-time. It is robust to strong illumination changes and factors out changes in appearance caused by illumination from changes due to face deformation. We adopt a model-based approach for facial expression recognition. In our model, an image of a face is represented by a point in a deformation space. The variability of the classes of images associated to facial expressions are represented by a set of samples which model a low-dimensional manifold in the space of deformations. We introduce a probabilistic procedure based on a nearest-neighbour approach to combine the information provided by the incoming image sequence with the prior information stored in the expression manifold in order to compute a posterior probability associated to a facial expression. In the experiments conducted we show that this system is able to work in an unconstrained environment with strong changes in illumination and face location. It achieves an 89\% recognition rate in a set of 333 sequences from the Cohn-Kanade data base