Personalized Automatic Estimation of Self-reported Pain Intensity from Facial Expressions

Pain is a personal, subjective experience that is commonly evaluated through visual analog scales (VAS). While this is often convenient and useful, automatic pain detection systems can reduce pain score acquisition efforts in large-scale studies by estimating it directly from the participants' facial expressions. In this paper, we propose a novel two-stage learning approach for VAS estimation: first, our algorithm employs Recurrent Neural Networks (RNNs) to automatically estimate Prkachin and Solomon Pain Intensity (PSPI) levels from face images. The estimated scores are then fed into the personalized Hidden Conditional Random Fields (HCRFs), used to estimate the VAS, provided by each person. Personalization of the model is performed using a newly introduced facial expressiveness score, unique for each person. To the best of our knowledge, this is the first approach to automatically estimate VAS from face images. We show the benefits of the proposed personalized over traditional non-personalized approach on a benchmark dataset for pain analysis from face images.Comment: Computer Vision and Pattern Recognition Conference, The 1st International Workshop on Deep Affective Learning and Context Modelin

Context-sensitive dynamic ordinal regression for intensity estimation of facial action units

Modeling intensity of facial action units from spontaneously displayed facial expressions is challenging mainly because of high variability in subject-specific facial expressiveness, head-movements, illumination changes, etc. These factors make the target problem highly context-sensitive. However, existing methods usually ignore this context-sensitivity of the target problem. We propose a novel Conditional Ordinal Random Field (CORF) model for context-sensitive modeling of the facial action unit intensity, where the W5+ (who, when, what, where, why and how) definition of the context is used. While the proposed model is general enough to handle all six context questions, in this paper we focus on the context questions: who (the observed subject), how (the changes in facial expressions), and when (the timing of facial expressions and their intensity). The context questions who and howare modeled by means of the newly introduced context-dependent covariate effects, and the context question when is modeled in terms of temporal correlation between the ordinal outputs, i.e., intensity levels of action units. We also introduce a weighted softmax-margin learning of CRFs from data with skewed distribution of the intensity levels, which is commonly encountered in spontaneous facial data. The proposed model is evaluated on intensity estimation of pain and facial action units using two recently published datasets (UNBC Shoulder Pain and DISFA) of spontaneously displayed facial expressions. Our experiments show that the proposed model performs significantly better on the target tasks compared to the state-of-the-art approaches. Furthermore, compared to traditional learning of CRFs, we show that the proposed weighted learning results in more robust parameter estimation from the imbalanced intensity data

A novel facial action intensity detection system

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of requirements for the degree of Master of Science. October 2014.Despite the fact that there has been quite a lot of research done in the eld of facial expression recognition, not much development has occurred in detecting the intensity of facial actions. In facial expression recognition, the intensity of facial actions is an important and crucial aspect, since it would provide more information about the facial expression of an individual, such as the level of emotion in a face. Furthermore, having an automated system that can detect the intensity of facial actions in an individual's face can lead up to a lot of potential applications from lie detection to smart classrooms. The provided approach includes robust methods for face and facial feature extraction, and multiple machine learning methods for facial action intensity detection

Statistical Modelling

The book collects the proceedings of the 19th International Workshop on Statistical Modelling held in Florence on July 2004. Statistical modelling is an important cornerstone in many scientific disciplines, and the workshop has provided a rich environment for cross-fertilization of ideas from different disciplines. It consists in four invited lectures, 48 contributed papers and 47 posters. The contributions are arranged in sessions: Statistical Modelling; Statistical Modelling in Genomics; Semi-parametric Regression Models; Generalized Linear Mixed Models; Correlated Data Modelling; Missing Data, Measurement of Error and Survival Analysis; Spatial Data Modelling and Time Series and Econometrics