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Link to original content: https://doi.org/10.1007/s00138-020-01071-8
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Robust face tracking using multiple appearance models and graph relational learning

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Abstract

This paper addresses the problem of appearance matching across different challenges while doing visual face tracking in real-world scenarios. In this paper, FaceTrack is proposed that utilizes multiple appearance models with its long-term and short-term appearance memory for efficient face tracking. It demonstrates robustness to deformation, in-plane and out-of-plane rotation, scale, distractors and background clutter. It integrates on the advantages of the tracking-by-detection by using a face detector that tackles the drastic scale appearance change of a face. A weighted score-level fusion strategy is proposed to obtain the face tracking output having the highest fusion score by generating candidates around possible face locations. FaceTrack showcases impressive performance when initiated automatically by outperforming several state-of-the-art trackers, except Struck by a very minute margin: 0.001 in precision and 0.017 in success, respectively.

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Notes

  1. Lowe [13] uses the ratio test to eradicate matches higher than 0.8. In FaceTrack experiments, 0.75 is used.

  2. The Gaussian kernel parameters are \(\sigma =6.0\), with a \(5\times 5\) filter size. The denominator, \(\varTheta \) of the exponential kernel is taken as 8000.0.

  3. Partial update: by replacing 12.5% of the face appearance features in ICM model and 10% of the face appearance features in BDM model, respectively, full update: by replacing 100% of the ICM and BDM model.

  4. Any other face detector can be used for initialization purpose.

  5. https://github.com/samhare/struck.

  6. https://github.com/gnebehay/CppMT.

  7. https://github.com/sinbycos/TUNA.

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Acknowledgements

This work was supported in part by FRQNT team Grant #167442 and by REPARTI (Regroupement pour l’étude des environnements partagés intelligents répartis) FRQNT strategic cluster.

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Authors and Affiliations

  1. LITIV Laboratory, Department of Computer and Software Engineering, Polytechnique Montreal, Montreal, QC, H3T1J4, Canada

    Tanushri Chakravorty & Guillaume-Alexandre Bilodeau

  2. LIVIA, Department of Systems Engineering, École de technologie supérieure, Montreal, QC, H3C1K3, Canada

    Éric Granger

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  1. Tanushri Chakravorty
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  2. Guillaume-Alexandre Bilodeau
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  3. Éric Granger
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Correspondence to Tanushri Chakravorty.

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Chakravorty, T., Bilodeau, GA. & Granger, É. Robust face tracking using multiple appearance models and graph relational learning. Machine Vision and Applications 31, 23 (2020). https://doi.org/10.1007/s00138-020-01071-8

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