Abstract
In this paper, research on the influence of parameters’ values in the pipelines of facial-based reidentification systems is presented. It was assumed that the solution should operate in real time in conditions typical of the reidentification system to be used. Such conditions were obtained as part of research regarding the reidentification of aggressively acting people during sports events. Typically, such a pipeline consists of many steps, including facial region detection, frontalisation, embedding, and classification, which are usually evaluated separately. This paper focuses on the parameters of facial alignment and classification in the context of systems based on well-established solutions of Multi-task Cascaded Convolutional Networks coupled with Inception Resnet embedding. The authors propose evaluating the results of the entire pipeline as a way to identify the optimal set of parameters for each step, thus producing a pipeline where the subsequent steps are best fitted to each other rather than giving the best results on their own.
The results indicate that the correct selection of parameters of the steps of the pipeline depends on further steps used and vice versa. It is therefore suboptimal to select parameters based on a separately evaluated set of steps, as it is usually presented in the literature. The reidentification pipeline must therefore be evaluated as a whole, disregarding the results achieved by any single part of the pipeline, as they are not an indicator of overall system performance.
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Acknowledgements
The research described in the paper was supported by grant no. WND-RPSL.01.02.00-24-00AC/19-011 “An innovative system for the identification and re-identification of people based on a facial image recorded in a short video sequence in order to increase the security of mass events.” funded under the Regional Operational Programme of the Silesia Voivodeship in the years 2014–2020.
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Pȩszor, D., Wojciechowski, K., Czarnecki, Ł. (2023). Influence of Step Parameterisation on the Results of the Reidentification Pipeline. In: Chmielewski, L.J., Orłowski, A. (eds) Computer Vision and Graphics. ICCVG 2022. Lecture Notes in Networks and Systems, vol 598. Springer, Cham. https://doi.org/10.1007/978-3-031-22025-8_11
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