Patients with schizophrenia often display impairments in the expression of emotion and speech and those are observed in their facial behaviour. Automatic analysis of patients' facial expressions that is aimed at estimating symptoms of schizophrenia has received attention recently. However, the datasets that are typically used for training and evaluating the developed methods, contain only a small number of patients (4-34) and are recorded while the subjects were performing controlled tasks such as listening to life vignettes, or answering emotional questions. In this paper, we use videos of professional-patient interviews, in which symptoms were assessed in a standardised way as they should/may be assessed in practice, and which were recorded in realistic conditions (i.e. varying illumination levels and camera viewpoints) at the patients' homes or at mental health services. We automatically analyse the facial behaviour of 91 out-patients -this is almost 3 times the number of patients in other studies -and propose SchiNet, a novel neural network architecture that estimates expression-related symptoms in two different assessment interviews. We evaluate the proposed SchiNet for patient-independent prediction of symptoms of schizophrenia. Experimental results show that some automatically detected facial expressions are significantly correlated to symptoms of schizophrenia, and that the proposed network for estimating symptom severity delivers promising results.
In this paper, we present a simple yet effective calibration method for multiple Kinects, i.e. a method that finds the rel ative position of RGB-depth cameras, as opposed to conven tional methods that find the relative position of RGB cameras. We first find the mapping function between the RGB camera and the depth camera mounted on one Kinect. With such a mapping function, we propose a scheme that is able to esti mate the 3D coordinates of the extracted corners from a stan dard calibration chessboard. To this end, we are able to build the 3D correspondences between two Kinects directly. This simplifies the calibration to a simple Least-Square Minimiza tion problem with very stable solution. Furthermore, by using two mirrored chessboard images on a thin board, we are able to calibrate two Kinects facing each other, something that is intractable using traditional calibration methods. We demon strate our proposed method with real data and show very accu rate calibration results, namely less than 7mm reconstruction error for objects at a distance of I.Sm, using around 7 frames for calibration.
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