Abstract
This paper proposes a probabilistic method for measuring and characterizing the complexity of gait samples represented by the well-known Gait Energy Image (GEI). The term complexity is here used to denote how singular (far from normality) a GEI is and how challenging might be its classification. We believe that a robust complexity index computed on a new gait sample could help to judge the reliability of the prediction given by a classifier. Experiments to assess the validity of the proposed measure and how it correlates with gait recognition effectiveness were conducted on two public databases covering both indoor and outdoor scenarios and variable covariate conditions.
This work has been partially supported by grants CSD2007–00018 and TIN2009–14205 from the Spanish Ministry of Education and Science, P1–1B2009–04 from the Fundació Caixa Castelló–Bancaixa, and PREDOC/2008/04 from Univ. Jaume I.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Moreno-Torres, J.G., Raeder, T., Alaiz-Rodríguez, R., Chawla, N.V., Herrera, F.: A unifying view on dataset shift in classification. Patt. Recog. 45(1), 521–530 (2012)
Bashir, K., Xiang, T., Gong, S.: Gait recognition without subject cooperation. Pattern Recognition Letters 31(13), 2052–2060 (2010)
Martín-Félez, R., Xiang, T.: Gait recognition by ranking. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. 7572, pp. 328–341. Springer, Heidelberg (2012)
Han, J., Bhanu, B.: Individual recognition using gait energy image. IEEE Trans. on PAMI 28(2), 316–322 (2006)
Winter, D.A.: Biomechanics and Motor Control of Human Movement, pp. i–xiv, John Wiley and Sons, Inc. (2009)
Bashir, K., Xiang, T., Gong, S.: Cross view gait recognition using correlation strength. In: BMVC, pp. 1–11 (2010)
Hossain, M., Makihara, Y., Junqui, W., Yagi, Y.: Clothes-invariant gait identification using part-based adaptive weight control. In: 19th ICPR, pp. 1–4 (2008)
Damen, D., Hogg, D.: Detecting carried objects in short video sequences. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 154–167. Springer, Heidelberg (2008)
Chen, C., Liang, J., Zhao, H., Hu, H., Tian, J.: Frame difference energy image for gait recognition with incomplete silhouettes. Pattern Recognition Letters 30(11), 977–984 (2009)
Matovski, D., Nixon, M., Mahmoodi, S., Mansfield, T.: On including quality in applied automatic gait recognition. In: 21st ICPR, pp. 3272–3275 (2012)
Chapelle, O., Keerthi, S.: Efficient algorithms for ranking with SVMs. Inf. Retrieval 13, 201–215 (2010)
Sarkar, S., Phillips, P., Liu, Z., Vega, I., Grother, P., Bowyer, K.: The HumanID gait challenge problem: data sets, performance, and analysis. IEEE Trans. on PAMI 27(2), 162–177 (2005)
Makihara, Y., Mannami, H., Tsuji, A., Hossain, M., Sugiura, K., Mori, A., Yagi, Y.: The OU-ISIR gait database comprising the treadmill dataset. Trans. on Computer Vision and Applications 4, 53–62 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ortells Lorenzo, J., Martín-Félez, R., Mollineda Cárdenas, R.A. (2013). A Complexity Measure of Gait Perception. In: Sanches, J.M., Micó, L., Cardoso, J.S. (eds) Pattern Recognition and Image Analysis. IbPRIA 2013. Lecture Notes in Computer Science, vol 7887. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38628-2_58
Download citation
DOI: https://doi.org/10.1007/978-3-642-38628-2_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38627-5
Online ISBN: 978-3-642-38628-2
eBook Packages: Computer ScienceComputer Science (R0)