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Auflistung nach Autor:in "Bigun, Josef"

1 - 4 von 4
  • Konferenzbeitrag
    Cross-eyed - cross-spectral iris/periocular recognition database and competition
    (Biosig 2016, 2016) Sequeira, Ana F.; Chen, Lulu; Ferryman, James; Alonso-Fernandez, Fernando; Bigun, Josef; Raja, Kiran B.; Ramachandra, Raghavendra; Busch, Christoph; Wild, Peter
  • Konferenzbeitrag
    Periocular Recognition Using CNN Features Off-the-Shelf
    (BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Hernandez-Diaz, Kevin; Alonso-Fernandez, Fernando; Bigun, Josef
    Periocular refers to the region around the eye, including sclera, eyelids, lashes, brows and skin. With a surprisingly high discrimination ability, it is the ocular modality requiring the least constrained acquisition. Here, we apply existing pre-trained architectures, proposed in the context of the ImageNet Large Scale Visual Recognition Challenge, to the task of periocular recognition. These have proven to be very successful for many other computer vision tasks apart from the detection and classification tasks for which they were designed. Experiments are done with a database of periocular images captured with a digital camera. We demonstrate that these off-the-shelf CNN features can effectively recognize individuals based on periocular images, despite being trained to classify generic objects. Compared against reference periocular features, they show an EER reduction of up to 40%, with the fusion of CNN and traditional features providing additional improvements.
  • Konferenzbeitrag
    Soft-Biometrics Estimation In the Era of Facial Masks
    (BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Alonso-Fernandez, Fernando; Diaz, Kevin Hernandez; Ramis, Silvia; Perales, Francisco J.; Bigun, Josef
    We analyze the use of images from face parts to estimate soft-biometrics indicators. Partial face occlusion is common in unconstrained scenarios, and it has become mainstream during the COVID-19 pandemic due to the use of masks. Here, we apply existing pre-trained CNN architectures, proposed in the context of the ImageNet Large Scale Visual Recognition Challenge, to the tasks of gender, age, and ethnicity estimation. Experiments are done with 12007 images from the Labeled Faces in the Wild (LFW) database. We show that such off-the-shelf features can effectively estimate soft-biometrics indicators using only the ocular region. For completeness, we also evaluate images showing only the mouth region. In overall terms, the network providing the best accuracy only suffers accuracy drops of 2-4% when using the ocular region, in comparison to using the entire face. Our approach is also shown to outperform in several tasks two commercial off-the-shelf systems (COTS) that employ the whole face, even if we only use the eye or mouth regions.
  • Konferenzbeitrag
    Symmetry assessment by finite expansion: application to forensic fingerprints
    (BIOSIG 2014, 2014) Mikaelyan, Anna; Bigun, Josef
    Common image features have too poor information for identification of forensic images of fingerprints, where only a small area of the finger is imaged and hence a small amount of key points are available. Noise, nonlinear deformation, and unknown rotation are additional issues that complicate identification of forensic fingerprints. We propose a feature extraction method which describes image information around key points: Symmetry Assessment by Finite Expansion (SAFE). The feature set has built-in quality estimates as well as a rotation invariance property. The theory is developed for continuous space, allowing compensation for features directly in the feature space when images undergo such rotation without actually rotating them. Experiments supporting that use of these features improves identification of forensic fingerprint images of the public NIST SD27 database are presented. Performance of matching orientation information in a neighborhood of core points has an EER of 24\% with these features alone, without using minutiae constellations, in contrast to 36\% when using minutiae alone. Rank-20 CMC is 58\%, which is lower than 67\% when using notably more manually collected minutiae information.