A principled approach to the analysis of eye movements for behavioural biometrics is laid down. The approach grounds in foraging theory, which provides a sound basis to capture the uniqueness of individual eye movement behaviour. We propose a composite Ornstein-Uhlenbeck process for quantifying the exploration/exploitation signature characterising the foraging eye behaviour. The relevant parameters of the composite model, inferred from eye-tracking data via Bayesian analysis, are shown to yield a suitable feature set for biometric identification; the latter is eventually accomplished via a classical classification technique. A proof of concept of the method is provided by measuring its identification performance on a publicly available dataset. Data and code for reproducing the analyses are made available. Overall, we argue that the approach offers a fresh view on either the analyses of eye-tracking data and prospective applications in this field.

Remote measurement of respiratory behaviour through RGB cameras has gained significant attention in the last couple of decades. Unlike traditional contact-based methods that may cause discomfort and require specialised equipment, contactless physiological measurement techniques offer a non-invasive way to monitor vital signs. In this survey article, we comprehensively review the literature and techniques related to estimating respiratory information from RGB cameras. We categorise the approaches into three main groups: methods utilising respiration-induced body movements, methods extracting respiratory information from blood volume pulse signals obtained via remote photoplethysmography, and deep learning-based techniques for direct respiratory signal extraction. To evaluate these approaches, we perform a comparative assessment using publicly available datasets. As a result, we uncover emerging trends while identifying strengths and weaknesses in the field. Our contributions include a detailed review of the literature, a benchmark of representative methods on multiple datasets, and the introduction of a new Python package called resPyre that implements the benchmarked approaches, making them accessible to the research community. This survey aims at promoting reproducibility, facilitate further research, and guide the development of more accurate and practical methods for remote respiration measurement using RGB cameras.

A core endeavour in current affective computing and social signal processing research is the construction of datasets embedding suitable ground truths to foster machine learning methods. This practice brings up hitherto overlooked intricacies. In this paper, we consider causal factors potentially arising when human raters evaluate the affect fluctuations of subjects involved in dyadic interactions and subsequently categorise them in terms of social participation traits. To gauge such factors, we propose an emulator as a statistical approximation of the human rater, and we first discuss the motivations and the rationale behind the approach.The emulator is laid down in the next section as a phenomenological model where the core affect stochastic dynamics as perceived by the rater are captured through an Ornstein–Uhlenbeck process; its parameters are then exploited to infer potential causal effects in the attribution of social traits. Following that, by resorting to a publicly available dataset, the adequacy of the model is evaluated in terms of both human raters’ emulation and machine learning predictive capabilities. We then present the results, which are followed by a general discussion concerning findings and their implications, together with advantages and potential applications of the approach.