Auflistung nach Autor:in "Alonso-Fernandez, Fernando"
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- KonferenzbeitragCross-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
- KonferenzbeitragFace tracking using optical flow development of a real-time adaboost cascade face tracker(BIOSIG 2015, 2015) Ranftl, Andreas; Alonso-Fernandez, Fernando; Karlsson, StefanIn this paper a novel face tracking approach is presented where optical flow information is incorporated into the Viola-Jones face detection algorithm. In the original algorithm from Viola and Jones face detection is static as information from previous frames is not considered. In contrast to the Viola- Jones face detector and also to other known dynamic enhancements, the proposed face tracker preserves information about near-positives. The algorithm builds a likelihood map from the intermediate results of the Viola-Jones algorithm which is extrapolated using optical flow. The objects get extracted from the likelihood map using image segmentation techniques. All steps can be computed very efficiently in real-time. The tracker is verified on the Boston Head Tracking Database showing that the proposed algorithm outperforms the standard Viola-Jones face detector.
- KonferenzbeitragPeriocular 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, JosefPeriocular 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.
- KonferenzbeitragSoft-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, JosefWe 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.