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Multimodal Gesture Recognition via Multiple Hypotheses Rescoring

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Gesture Recognition

Part of the book series: The Springer Series on Challenges in Machine Learning ((SSCML))

Abstract

We present a new framework for multimodal gesture recognition that is based on a multiple hypotheses rescoring fusion scheme. We specifically deal with a demanding Kinect-based multimodal dataset, introduced in a recent gesture recognition challenge (CHALEARN 2013), where multiple subjects freely perform multimodal gestures. We employ multiple modalities, that is, visual cues, such as skeleton data, color and depth images, as well as audio, and we extract feature descriptors of the hands’ movement, handshape, and audio spectral properties. Using a common hidden Markov model framework we build single-stream gesture models based on which we can generate multiple single stream-based hypotheses for an unknown gesture sequence. By multimodally rescoring these hypotheses via constrained decoding and a weighted combination scheme, we end up with a multimodally-selected best hypothesis. This is further refined by means of parallel fusion of the monomodal gesture models applied at a segmental level. In this setup, accurate gesture modeling is proven to be critical and is facilitated by an activity detection system that is also presented. The overall approach achieves 93.3% gesture recognition accuracy in the CHALEARN Kinect-based multimodal dataset, significantly outperforming all recently published approaches on the same challenging multimodal gesture recognition task, providing a relative error rate reduction of at least 47.6%.

Editors: Isabelle Guyon, Vassilis Athitsos and Sergio Escalera

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Notes

  1. 1.

    For the case of video data an observation corresponds to a single image frame; for the case of audio modality it corresponds to a 25 msec window.

  2. 2.

    That is, transformed to have zero mean and a standard deviation of one.

  3. 3.

    The \(w'_m\) are different from the weights in (16.1). Their selection is similarly based on a separate validation set that is distinct from the final evaluation set; more details on the selection of weights are provided in Sect. 16.5.

  4. 4.

    These parameters are set after experimentation in a single video of the validation set, that was annotated in terms of activity.

  5. 5.

    Parameter ranges in the experiments for each modality are as follows. Audio: States 10–28, Gaussians: 2–32; Skeleton/Handshape: States 7–15, Gaussians: 2–10.

  6. 6.

    For the measurements we employed an AMD Opteron(tm) Processor 6386 at 2.80 GHz with 32 GB RAM.

  7. 7.

    The weights take values in [0, 1] while their sum across the modalities adds to one; these values are then scaled by 100 for the sake of numerical presentation. For the w stream weights we sampled the [0, 1] with 12 samples for each modality, resulting to 1728 combinations. For the \(w'\) case, we sampled the [0, 1] space by employing 5, 5 and 21 samples for the gesture, handshape and speech modalities respectively, resulting on 525 combinations.

  8. 8.

    Note that the Levensthein distance takes values in [0, 1] and is equivalent to the word error rate.

  9. 9.

    D1-3 notation refers to D1, D2 and D3 cases.

  10. 10.

    All relative percentages, unless stated otherwise, refer to relative LD reduction (LDR). LDR is equivalent to the known relative word error rate reduction.

  11. 11.

    Statistical significance tests are computed on the raw recognition values and not on the relative improvement scores.

  12. 12.

    In all results presented we follow the same blind testing rules that hold in the challenge, in which we have participated (pptk team). In Table 16.3 we include for common reference the Levenshtein distance (LD) which was also used in the challenge results (Escalera et al. 2013b).

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Acknowledgements

This research work was supported by the European Union under the project “MOBOT” with grant FP7-ICT-2011-9 2.1 - 600796. The authors want to gratefully thank G. Pavlakos for his contribution in previous, earlier stages, of this work. This work was done while V. Pitsikalis and S. Theodorakis were both with the National Technical University of Athens; they are now with deeplab.ai, Athens, GR.

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  1. School of Electrical and Computer Engineering, National Technical University of Athens, Zografou Campus, 15773, Athens, Greece

    Vassilis Pitsikalis, Athanasios Katsamanis, Stavros Theodorakis & Petros Maragos

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  1. Vassilis Pitsikalis
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  1. University of Barcelona, Barcelona, Spain

    Sergio Escalera

  2. ChaLearn, Berkeley, California, USA

    Isabelle Guyon

  3. Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, Texas, USA

    Vassilis Athitsos

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Pitsikalis, V., Katsamanis, A., Theodorakis, S., Maragos, P. (2017). Multimodal Gesture Recognition via Multiple Hypotheses Rescoring. In: Escalera, S., Guyon, I., Athitsos, V. (eds) Gesture Recognition. The Springer Series on Challenges in Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-319-57021-1_16

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