Who are you? - real-time person identification

This paper presents a system for person identification that uses concise statis-tical models of facial features in a real-time realisation of the cast identifica-tion system of Everingham et al. [7]. Our system integrates the cascaded face detector of Viola and Jones with a kernel-based regressor for face tracking, which is trained on-line when new people are detected in the video stream. A pictorial model is used to compute the locations of facial features, which form a descriptor of the person’s face. When sufficient samples are collected, identification is performed using a random-ferns classifier by marginalising over the facial features. This confers robustness to localisation errors and occlusions, while enabling a real-time search of the database. These four different processes communicate within a real-time framework capable of tracking and identifying up to 5 people in real-time on a standard dual-core 1.86GHz machine.

Mirroring to Build Trust in Digital Assistants

We describe experiments towards building a conversational digital assistant that considers the preferred conversational style of the user. In particular, these experiments are designed to measure whether users prefer and trust an assistant whose conversational style matches their own. To this end we conducted a user study where subjects interacted with a digital assistant that responded in a way that either matched their conversational style, or did not. Using self-reported personality attributes and subjects' feedback on the interactions, we built models that can reliably predict a user's preferred conversational style.Comment: Preprin

Bayesian Video Matting Using Learnt Image Priors

This paper presents a method inspired by natural image statistics where a second order prior is learnt that models the relationship between the spatiotemporal gradients in the image sequence and those in the alpha mattes. This is used in combination with a learnt foreground color model and a prior on the alpha distribution to help regularize the solution and greatly improve the automatic performance of the syste

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One of the more startling effects of road related accidents is the economic and social burden that they cause. In OECD countries (the 23 leading economically developed countries of the world) over 150,000 people are killed every year (44,000+ in the USA, 38,000+ in Europe and 11,000+ in Japan) at an estimated cost of US $ 500 billion. One way of combating this problem is to develop intelligent vehicles that are self-aware and act to increase the safety of the transportation system. In this paper we present preliminary results of an Intelligent Transport System project that has fused visual lane tracking and driver monitoring technologies in the first step towards closing the loop between vision inside and outside the vehicle. Experimental results of a novel 15 Hz visual lane tracking system will be discussed, focusing on the particle filter and cue fusion technology used. The results from the integration of the lane tracker an