Cluster-analytic classification of facial expressions using infrared measurements of facial thermal features

In previous research, scientists were able to use transient facial thermal features extracted from Thermal Infra-Red Images (TIRIs) for making binary distinction between the affective states. For example, thermal asymmetries localised in facial TIRIs have been used to distinguish anxiety and deceit. Since affective human-computer interaction would require machines to distinguish between the subtle facial expressions of affective states, computers’ able to make such binary distinctions would not suffice a robust human-computer interaction. This work, for the first time, uses affective-state-specific transient facial thermal features extracted from TIRIs to recognise a much wider range of facial expressions under a much wider range of conditions. Using infrared thermal imaging within the 8-14 μm, a database of 324 discrete, time-sequential, visible-spectrum and thermal facial images was acquired, representing different facial expressions from 23 participants in different situations. A facial thermal feature extraction and pattern classification approach was developed, refined and tested on various Gaussian mixture models constructed using the image database. Attempts were made to classify: neutral and pretended happy and sad faces; multiple positive and negative facial expressions; six (pretended) basic facial expressions; partially covered or occluded faces; and faces with evoked happiness, sadness, disgust and anger. The cluster-analytic classification in this work began by segmentation and detection of thermal faces in the acquired TIRIs. The affective-state-specific temperature distributions on the facial skin surface were realised through the pixel grey-level analysis. Examining the affectivestate- specific temperature variations within the selected regions of interest in the TIRIs led to the discovery of some significant Facial Thermal Feature Points (FTFPs) along the major facial muscles. Following a multivariate analysis of the Thermal Intensity values (TIVs) measured at the FTFPs, the TIRIs were represented along the Principal Components (PCs) of a covariance matrix. The resulting PCs were ranked in the order of their effectiveness in the between-cluster separation. Only the most effective PCs were retained to construct an optimised eigenspace. A supervised learning algorithm was invoked for linear subdivision of the optimised eigenspace. The statistical significance levels of the classification results were estimated for validating the discriminant functions. The main contribution of this research has been to show that: the infrared imaging of facial thermal features within the 8-14 μm bandwidth may be used to observe affective-state-specific thermal variations on the face; the pixel-grey level analysis of TIRIs can help localise FTFPs along the major facial muscles of the face; cluster-analytic classification of transient thermal features may help distinguish between the facial expressions of affective states in an optimized eigenspace of input thermal feature vectors. The Gaussian mixture model with one cluster per affect worked better for some facial expressions than others. This made the influence of the Gaussian mixture model structure on the accuracy of the classification results obvious. However, the linear discrimination and confusion patterns observed in this work were consistent with the ones reported in several earlier studies. This investigation also unveiled some important dimensions of the future research on use of facial thermal features in affective human-computer interaction.