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  1. Free, publicly-accessible full text available October 1, 2024
  2. Multistage, or serial, fusion refers to the algorithms sequentially fusing an increased number of matching results at each step and making decisions about accepting or rejecting the match hypothesis, or going to the next step. Such fusion methods are beneficial in the situations where running additional matching algorithms needed for later stages is time consuming or expensive. The construction of multistage fusion methods is challenging, since it requires both learning fusion functions and finding optimal decision thresholds for each stage. In this paper, we propose the use of single neural network for learning the multistage fusion. In addition we discuss the choices for the performance measurements of the trained algorithms and for the selection of network training optimization criteria. We perform the experiments using three face matching algorithms and IJB-A and IJB-C databases. 
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  3. The security of biometric templates is of paramount importance. Leakage of biometric information may result in loss of private data and can lead to the compromise of the biometric system. Yet, the security of templates is often overlooked in favour of performance. In this paper, we present a plug-and-play framework for creating secure face templates with negligible degradation in the performance of the system. We propose a significant bit based representation which guarantees security in addition to other biometric aspects such as cancelability and reproducibility. In addition to being scalable, the proposed method does not make unrealistic assumptions regarding the pose or illumination of the face images. We provide experimental results on two unconstrained datasets - IJB-A and IJB-C. 
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