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Auflistung nach Autor:in "Spreeuwers, Luuk J."

1 - 4 von 4
  • Konferenzbeitrag
    Automatic landmark detection and face recognition for side-view face images
    (BIOSIG 2013, 2013) Santemiz, Pinar; Spreeuwers, Luuk J.; Veldhuis, Raymond N. J.
    In real-life scenarios where pose variation is up to side-view positions, face recognition becomes a challenging task. In this paper we propose an automatic sideview face recognition system designed for home-safety applications. Our goal is to recognize people as they pass through doors in order to determine their location in the house. Here, we introduce a recognition method, where we detect facial landmarks automatically for registration and identify faces. We test our system on side-view face images from CMU-Multi PIE database. We achieve 95 95% accuracy on detecting . landmarks, and 89 04% accuracy on identification. .
  • Konferenzbeitrag
    Evaluation of automatic face recognition for automatic border control on actual data recorded of travellers at Schiphol Airport
    (BIOSIG 2012, 2012) Spreeuwers, Luuk J.; Hendrikse, Anne J.; Gerritsen, Kier-Co J.
    Automatic border control at airports using automated facial recognition for checking the passport is becoming more and more common. A problem is that it is not clear how reliable these automatic gates are. Very few independent studies exist that assess the reliability of automated facial recognition for border control. In this evaluation study the reliability of automated facial recognition for automatic border passage was investigated. To investigate the quality of the images and face recognition, during 2 weeks data of real passengers were acquired at Schiphol Airport using 2 different automatic gates of about 950 passengers for both gates. This data alone already makes the evaluation study of great value. The evaluation experiment consisted of comparing live images of every passenger to the digital photographs stored on their passports. Every live image is compared to every digital passport photograph. In this way we can estimate both the False Accept Rate as well as the Verification Rate. In spite of the critical analysis in this study, the prospects for automatic border passage using face recognition are very good. We expect that, provided that the quality of the live images acquired by the gates is improved and if possible the quality of the digital photographs stored on the passport, excellent recognition results can be obtained with Verification Rates (VR) of above 99% at a False Accept Rate (FAR) of 0.1% or even lower.
  • Konferenzbeitrag
    Fixed FAR vote fusion of regional facial classifiers
    (BIOSIG 2014, 2014) Spreeuwers, Luuk J.; Veldhuis, Raymond N. J.; Sultanali, Siar; Diephuis, Jasper
    Holistic face recognition methods like PCA and LDA have the disadvantage that they are very sensitive to expression, hair and illumination variations. This is one of the main reasons they are no longer competitive in the major benchmarks like FRGC and FRVT. In this paper we present an LDA based approach that combines many overlapping regional classifiers (experts) using what we call a Fixed FAR Voting Fusion (FFVF) strategy. The combination by voting of regional classifiers means that if there are sufficient regional classifiers unaffected by the expression, illumination or hair variations, the fused classifier will still correctly recognise the face. The FFVF approach has two interesting properties: it allows robust fusion of dependent classifiers and it only requires a single parameter to be tuned to obtain weights for fusion of different classifiers. We show the potential of the FFVF of regional classifiers using the standard benchmarks experiments 1 and 4 on FRGCv2 data. The multi-region FFVF classifier has a FRR of 4\% at FAR=0.1\% for controlled and 38\% for uncontrolled data compared to 7\% and 56\% for the best single region classifier.
  • Konferenzbeitrag
    Identification performance of evidential value estimation for fingermarks
    (BIOSIG 2015, 2015) Kotzerke, Johannes; Davis, Stephen A.; Hayes, Robert; Spreeuwers, Luuk J.; Veldhuis, Raymond N. J.; Horadam, Kathy J.
    Law enforcement agencies around the world use biometrics and fingerprints to solve and fight crime. Forensic experts are needed to record fingermarks at crime scenes and to ensure those captured are of evidential value. This process needs to be automated and streamlined as much as possible to improve efficiency and reduce workload. It has previously been demonstrated that is possible to estimate a fingermark's evidential value automatically for image captures taken with a mobile phone or other devices, such as a scanner or a high-quality camera. Here we study the relationship between a fingermark being of evidential value and its correct and certain identification and if it is possible to achieve identification despite the mark not having sufficient evidential value. Subsequently, we also investigate the influence the capture device used makes and if a mobile phone is an option worth considering. Our results show that automatic identification is possible for 126 of the 1 428 fin- , germarks captured by a mobile phone, of which 116 were marked as having evidential value by experts and 123 by an automated algorithm.