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Face Recognition in Videos – A Graph Based Modified Kernel Discriminant Analysis

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Computer Vision – ACCV 2012 (ACCV 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7724))

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Abstract

Grassmannian manifolds have been an effective way to represent image sets (video) which are mapped as data points on the manifold. Recognition can then be performed by applying the Discriminant Analysis (DA) on such manifolds. However, the local structure of the data points are not exploited in the DA. This paper proposes a modified Kernel Discriminant Analysis (KDA) approach on Grassmannian manifolds that utilizes the local structure of the data points on the manifold. The KDA exploits the local structure using between-class and within-class adjacency graphs that represent the between-class and within-class similarities, respectively. The maximum correlation from within-class and minimum correlation from between-class is utilized to define the connectivity between points in the graph thus exploiting the geometrical structure of the data. The discriminability is further improved by effective feature representation using LBP which can discriminate data across illumination, pose, and minor expressions. Effective recognition is performed by using only the cluster representatives extracted by clustering the frames of a video sequence. Experiments on several video datasets (Honda, MoBo, ChokePoint, NRC-IIT, and MOBIO) show that the proposed approach obtains better recognition rates, in comparison with the state-of-the-art approaches.

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Author information

Authors and Affiliations

  1. Video/Image Modeling and Synthesis Laboratory (VIMS), Dept. of Computer and Information Sciences, University of Delaware, Newark, DE, 19711, USA

    Gayathri Mahalingam & Chandra Kambhamettu

Authors
  1. Gayathri Mahalingam
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  2. Chandra Kambhamettu
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, 151-744, Gwanak-gu, Seoul, Korea

    Kyoung Mu Lee

  2. Microsoft Research Asia, No. 5, Danling st., Haidian district, 100080, Beijing, P.R. China

    Yasuyuki Matsushita

  3. School of Interactive Computing, Georgia Institute of Technology, 801 Atlantic Drive, CCB 315, 30332, Atlanta, GA, USA

    James M. Rehg

  4. Institute of Automation, National Laboratory of Pattern Recognition, Chinese Academy of Sciences, Zhong Quan Cun East Road 95, Haidian District, 100 190, Beijing, P.R. China

    Zhanyi Hu

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Mahalingam, G., Kambhamettu, C. (2013). Face Recognition in Videos – A Graph Based Modified Kernel Discriminant Analysis. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37331-2_28

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  • DOI: https://doi.org/10.1007/978-3-642-37331-2_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37330-5

  • Online ISBN: 978-3-642-37331-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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