Influence of Step Parameterisation on the Results of the Reidentification Pipeline

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.