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Optimal face templates: the next step in surveillance face recognition

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A Correction to this article was published on 24 September 2019

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Abstract

The paper deals with surveillance face recognition in security applications such as surveillance camera systems or access control systems. Presented research is focused on enhancing recognition performance, reducing classification time and memory requirements. We aim to make it feasible to implement face recognition in end devices such as cameras, identification terminals or popular IoT devices. Therefore, we utilize algorithms that require low computational power and optimize them in order to reach higher recognition rates. We present a novel higher quantile method that enhances recognition performance via creation of robust and representative face templates for nearest neighbor classifier. Templates computed by the higher quantile method are determined by tolerance intervals which handle feature variability caused by face pose, expression, illumination and possible low image quality. The recognition performance evaluation has been conducted on images captured by surveillance camera system that are contained in unique IFaViD dataset. The IFaViD is the only one dataset captured by real surveillance camera system containing complex scenarios. The results show that the higher quantile method outperforms the contemporary approaches by 4%, respectively, 10% depending on the IFaViD’s test subset.

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  • 24 September 2019

    In the original publication of the article, the below-mentioned acknowledgement was not included and the author would like to add it.

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Authors and Affiliations

  1. EBIS, spol. s r.o., Krizikova 2962/70a, Brno, Czech Republic

    Tobias Malach

  2. Department of Radio Electronics, Brno University of Technology, Technicka 12, Brno, Czech Republic

    Tobias Malach & Jitka Pomenkova

Authors
  1. Tobias Malach
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  2. Jitka Pomenkova
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Correspondence to Tobias Malach.

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Malach, T., Pomenkova, J. Optimal face templates: the next step in surveillance face recognition. Pattern Anal Applic 23, 1021–1032 (2020). https://doi.org/10.1007/s10044-019-00842-y

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