Search results for "Linear time invariant system"

showing 2 items of 2 documents

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

2015

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…

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 matrix2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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Hankelet-based dynamical systems modeling for 3D action recognition

2015

This paper proposes to model an action as the output of a sequence of atomic Linear Time Invariant (LTI) systems. The sequence of LTI systems generating the action is modeled as a Markov chain, where a Hidden Markov Model (HMM) is used to model the transition from one atomic LTI system to another. In turn, the LTI systems are represented in terms of their Hankel matrices. For classification purposes, the parameters of a set of HMMs (one for each action class) are learned via a discriminative approach. This work proposes a novel method to learn the atomic LTI systems from training data, and analyzes in detail the action representation in terms of a sequence of Hankel matrices. Extensive eval…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniSequenceMarkov chainDynamical systems theorySupervised learningHankel MatrixHidden Markov ModelLTI system theoryDiscriminative learningLinear time invariant systemDiscriminative modelActionComputer Science::Systems and ControlControl theorySignal ProcessingComputer Vision and Pattern RecognitionElectrical and Electronic EngineeringHidden Markov modelHankel matrixAlgorithmMathematicsImage and Vision Computing
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