6533b853fe1ef96bd12ac0b0

RESEARCH PRODUCT

Using Hankel matrices for dynamics-based facial emotion recognition and pain detection

Liliana Lo PrestiMarco La Cascia

subject

FOS: Computer and information sciencesComputer Science - Artificial IntelligenceComputer Vision and Pattern Recognition (cs.CV)Speech recognitionFeature extractionComputer Science - Computer Vision and Pattern RecognitionPainLTI system theoryComputer Science - RoboticsLinear time invariant systemRepresentation (mathematics)Hidden Markov modelMathematicsEmotionSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniSequencebusiness.industryPattern recognitiondynamicsClassificationSupport vector machineArtificial Intelligence (cs.AI)Face (geometry)Artificial intelligencebusinessRobotics (cs.RO)Hankel matrix

description

This paper proposes a new approach to model the temporal dynamics of a sequence of facial expressions. To this purpose, a sequence of Face Image Descriptors (FID) is regarded as the output of a Linear Time Invariant (LTI) system. The temporal dynamics of such sequence of descriptors are represented by means of a Hankel matrix. The paper presents different strategies to compute dynamics-based representation of a sequence of FID, and reports classification accuracy values of the proposed representations within different standard classification frameworks. The representations have been validated in two very challenging application domains: emotion recognition and pain detection. Experiments on two publicly available benchmarks and comparison with state-of-the-art approaches demonstrate that the dynamics-based FID representation attains competitive performance when off-the-shelf classification tools are adopted.

yearjournalcountryeditionlanguage
2015-01-012015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
https://doi.org/10.1109/cvprw.2015.7301351