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Improved Low Resolution Heterogeneous Face Recognition Using Re-ranking

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Computer Vision, Pattern Recognition, Image Processing, and Graphics (NCVPRIPG 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 841))

Abstract

Recently, near-infrared to visible light facial image matching is gaining popularity, especially for low-light and night-time surveillance scenarios. Unlike most of the work in literature, we assume that the near-infrared probe images have low-resolution in addition to uncontrolled pose and expression, which is due to the large distance of the person from the camera. To address this very challenging problem, we propose a re-ranking strategy which takes into account the relation of both the probe and gallery with a set of reference images. This can be used as an add-on to any existing algorithm. We apply it with one recent dictionary learning algorithm which uses alignment of orthogonal dictionaries. We also create a benchmark for this task by evaluating some of the recent algorithms for this experimental protocol. Extensive experiments are conducted on a modified version of the CASIA NIR VIS 2.0 database to show the effectiveness of the proposed re-ranking approach.

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Acknowledgment

This work is partly supported through a research grant from DeITY, India.

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Authors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Science, Bangalore, India

    Sivaram Prasad Mudunuri, Shashanka Venkataramanan & Soma Biswas

Authors
  1. Sivaram Prasad Mudunuri
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  2. Shashanka Venkataramanan
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  3. Soma Biswas
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Corresponding author

Correspondence to Soma Biswas .

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Editors and Affiliations

  1. Indian Institute of Technology Mandi, Mandi, Himachal Pradesh, India

    Renu Rameshan

  2. Indraprastha Institute of Information Technology, New Delhi, India

    Chetan Arora

  3. Indian Institute of Technology, New Delhi, India

    Sumantra Dutta Roy

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Mudunuri, S.P., Venkataramanan, S., Biswas, S. (2018). Improved Low Resolution Heterogeneous Face Recognition Using Re-ranking. In: Rameshan, R., Arora, C., Dutta Roy, S. (eds) Computer Vision, Pattern Recognition, Image Processing, and Graphics. NCVPRIPG 2017. Communications in Computer and Information Science, vol 841. Springer, Singapore. https://doi.org/10.1007/978-981-13-0020-2_39

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  • DOI: https://doi.org/10.1007/978-981-13-0020-2_39

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-0019-6

  • Online ISBN: 978-981-13-0020-2

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