Logo des Repositoriums

Auflistung nach Schlagwort "fusion"

1 - 3 von 3
  • Konferenzbeitrag
    Evaluating Face Image Quality Score Fusion for Modern Deep Learning Models
    (BIOSIG 2022, 2022) Schlett, Torsten; Rathgeb, Christian; Tapia, Juan E.; Busch, Christoph
    Face image quality assessment algorithms attempt to estimate the utility of face images for biometric systems, typically face recognition, since the performance of these systems can be limited by the image quality. Hand-crafted quality score fusion has previously been examined for a variety of mostly factor-specific quality assessment algorithms. This paper instead examines score fusion for various recent “monolithic” quality assessment deep learning models. The evaluation methodology is based on Error-versus-Reject-Characteristic partial-Area-Under-Curve values, which are used to quantitatively rank quality assessment configurations in a face recognition context. Mean quality score fusion configurations were found to slightly improve performance on the TinyFace database, while the tested fusion types were ineffective on the LFW database.
  • Konferenzbeitrag
    Fusion of Face Demorphing and Deep Face Representations for Differential Morphing Attack Detection
    (BIOSIG 2022, 2022) Shiqerukaj, Elidona; Rathgeb, Christian; Merkle, Johannes; Drozdowski, Pawel; Tams, Benjamin
    Algorithm fusion is frequently employed to improve the accuracy of pattern recognition tasks. This particularly applies to biometrics including attack detection mechanisms. In this work, we apply a fusion of two differential morphing attack detection methods, i.e. Demorphing and Deep Face Representations. Experiments are performed in a cross-database scenario using high-quality face morphs along with realistic live captures. Obtained results reveal that a weighted sum-based score-level fusion of Demorphing and Deep Face Representations improves the morphing attack detection accuracy. With the proposed fusion, a detection equal error rate of 4.9% is achieved, compared to detection equal error rates of 5.6% and 5.8% of the best individual morphing attack detection methods, respectively.
  • Konferenzbeitrag
    PROTECT Multimodal DB: fusion evaluation on a novel multimodal biometrics dataset envisaging Border Control
    (BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Sequeira, Ana F.; Chen, Lulu; Ferryma, James; Galdi, Chiara; Chiesa, Valeria; Dugelay, Jean-Luc; Maik, Patryk; Gmitrowicz, Piotr; Szklarski, Lukasz; Prommegger, Bernhard; Kauba, Christof; Kirchgasser, Simon; Uhl, Andreas; Grudzien, Artur; Kowalski, Marcin
    This work presents a novel multimodal database comprising 3D face, 2D face, thermal face, visible iris, finger and hand veins, voice and anthropometrics. This dataset will constitute a valuable resource to the field with its number and variety of biometric traits. Acquired in the context of the EU PROTECT project, the dataset allows several combinations of biometric traits and envisages applications such as border control. Based upon the results of the unimodal data, a fusion scheme was applied to ascertain the recognition potential of combining these biometric traits in a multimodal approach. Due to the variability on the discriminative power of the traits, a leave the n-best out fusion technique was applied to obtain different recognition results.