title: Improving Pain Recognition Through Better Utilisation of Temporal Information.
abstract: Automatically recognizing pain from video is a very useful application as it has the potential to alert carers to patients that are in discomfort who would otherwise not be able to communicate such emotion (i.e young children, patients in postoperative care etc.). In previous work [1], a "pain-no pain" system was developed which used an AAM-SVM approach to good effect. However, as with any task involving a large amount of video data, there are memory constraints that need to be adhered to and in the previous work this was compressing the temporal signal using K-means clustering in the training phase. In visual speech recognition, it is well known that the dynamics of the signal play a vital role in recognition. As pain recognition is very similar to the task of visual speech recognition (i.e. recognising visual facial actions), it is our belief that compressing the temporal signal reduces the likelihood of accurately recognising pain. In this paper, we show that by compressing the spatial signal instead of the temporal signal, we achieve better pain recognition. Our results show the importance of the temporal signal in recognizing pain, however, we do highlight some problems associated with doing this due to the randomness of a patient's facial actions.
authors.affiliation: Speech, Audio, Image and Video Technology Laboratory, Queensland University of Technology, Brisbane, QLD, 4000, Australia.
authors.forename: Patrick
authors.initials: P
authors.lastname: Lucey
authors.affiliation: Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15232, USA.
authors.forename: Jessica
authors.initials: J
authors.lastname: Howlett
authors.affiliation: Department of Psychology, University of Pittsburgh, Pittsburgh, PA 15260, USA.
authors.forename: Jeff
authors.initials: J
authors.lastname: Cohn
authors.forename: Simon
authors.initials: S
authors.lastname: Lucey
authors.forename: Sridha
authors.initials: S
authors.lastname: Sridharan
authors.forename: Zara
authors.initials: Z
authors.lastname: Ambadar
keywords: action units (AUs); active appearance models (AAM); facial expression; pain; visual speech recognition