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

Is spoken language all-or-nothing? Implications for future speech-based human-machine interaction

Recent years have seen significant market penetration for voice-based personal assistants such as Apple’s Siri. However, despite this success, user take-up is frustratingly low. This article argues that there is a habitability gap caused by the inevitablemismatch between the capabilities and expectations of human users and the features and benefits provided by contemporary technology. Suggestions aremade as to how such problems might be mitigated, but a more worrisome question emerges: “is spoken language all-or-nothing”? The answer, based on contemporary views on the special nature of (spoken) language, is that there may indeed be a fundamental limit to the interaction that can take place between mismatched interlocutors (such as humans and machines). However, it is concluded that interactions between native and non-native speakers, or between adults and children, or even between humans and dogs, might provide critical inspiration for the design of future speech-based human-machine interaction