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Auflistung nach Autor:in "Ortega-Garcia, Javier"

1 - 2 von 2
  • Konferenzbeitrag
    Pose Variability Compensation Using Projective Transformation Forensic Face Recognition
    (BIOSIG 2015, 2015) Gonzalez-Sosa, Ester; Vera-Rodriguez, Ruben; Fierrez, Julian; Tome, Pedro; Ortega-Garcia, Javier
    The forensic scenario is a very challenging problem within the face recognition community. The verification problem in this case typically implies the comparison between a high quality controlled image against a low quality image extracted from a close circuit television (CCTV). One of the downsides that frequently presents this scenario is pose deviation since CCTV devices are usually placed in ceilings and the subject normally walks facing forward. This paper proves the value of the projective transformation as a simple tool to compensate the pose distortion present in surveillance images in forensic scenarios. We evaluate the influence of this projective transformation over a baseline system based on principal component analysis and support vector machines (PCA-SVM) for the SCface database. The application of this technique improves greatly the performance, being this improvement more striking with closer images. Results suggest the convenience of this transformation within the preprocessing stage of all CCTV images. The average relative improvement reached with this method is around 30\% of EER.
  • Konferenzbeitrag
    Towards Fingerprint Presentation Attack Detection Based on Convolutional Neural Networks and Short Wave Infrared Imaging
    (BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Tolosana, Ruben; Gomez-Barrero, Marta; Kolberg, Jascha; Morales, Aythami; Busch, Christoph; Ortega-Garcia, Javier
    Biometric recognition offers many advantages over traditional authentication methods, but they are also vulnerable to, for instance, presentation attacks. These refer to the presentation of artifacts, such as facial pictures or gummy fingers, to the biometric capture device, with the aim of impersonating another person or to avoid being recognised. As such, they challenge the security of biometric systems and must be prevented. In this paper, we present a new fingerprint presentation attack detection method based on convolutional neural networks and multi-spectral images extracted from the finger in the short wave infrared spectrum. The experimental evaluation, carried out on an initial small database but comprising different materials for the fabrication of the artifacts and including unknown attacks for testing, shows promising results: all samples were correctly classified.