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Auflistung nach Schlagwort "Periocular"

1 - 4 von 4
  • Konferenzbeitrag
    Contactless Palmprint Recognition for Children
    (BIOSIG 2023, 2023) Akash M Godbole, Steven A Grosz
    Effective distribution of nutritional and healthcare aid for children, particularly infants and toddlers, in the world’s least developed and most impoverished countries, is a major problem due to lack of reliable identification documents. We present a mobile based contactless palmprint recognition system, Child Palm-ID, which meets the requirements of usability, cost, and accuracy for child recognition. On a contactless child palmprint database, Child-PalmDB1, with 1,020 unique palms (age range of 6 mos. to 48 mos.), Child Palm-ID achieves a TAR=94.8% at FAR=0.1%. Child Palm-ID is also able to recognize adults, achieving a TAR=99.5% on the CASIA contactless palmprint database and a TAR=100% on the COEP contactless adult palmprint database, both at FAR=0.1%. For child palmprint images captured at an interval of five months with differences in standoff distance, illumination and motion blur, the TAR drops to 80.5% at FAR=0.1%. This indicates that more research remains in contactless child palmprint recognition.
  • Konferenzbeitrag
    Exploring the Untapped Potential of Unsupervised Representation Learning for Training Set Agnostic Finger Vein Recognition
    (BIOSIG 2023, 2023) Tugce Arican, Raymond Veldhuis
    Finger vein patterns are a promising biometric trait because of their higher privacy and security features compared to face and finger prints. Finger vein recognition methods have been researched extensively, especially deep learning based methods such as Convolutional Neural Networks. These methods show promising recognition performance, but their low degree of generalization and adaptability results in much lower and inconsistent recognition performance in cross database scenarios. Despite these drawbacks, much less research has gone into the generalization and adaptability of these deep learning methods. This study addresses these issues and proposes an unsupervised learning approach, namely a patch-based Convolutional Auto-encoder for learning finger vein representations. Our proposed approach outperforms traditional baseline finger recognition methods on the UTFVP, SDUMLA-HMT, and PKU datasets, and achieves state-of-the-art performance on the UTFVP dataset with 0.24\% EER. It also indicates a noticeably higher generalization of finger vein features across different datasets compared to a supervised method. The findings of this work offer promising advancements in achieving robust finger vein recognition in real-life scenarios, due to the enhanced generalization and adaptability of our proposed method.
  • Konferenzbeitrag
    On the Impact of Tattoos on Hand Recognition
    (BIOSIG 2023, 2023) Lazaro Janier Gonzalez-Soler, Kacper Marek Zyla
    From Native Americans, who used tattoos as a way of seducing the opposite sex, to prisoners in the last century, who were identified by tattooed numbers, tattoos have been used for many years for a variety of purposes. Nowadays, tattoos express affiliation or beliefs and can therefore serve as complementary information to identify individuals. To support forensic investigations, hand-based biometrics have emerged as a promising technology to recognise individuals. As several statistics have reported an increase in the use of tattoos on hands, in this paper, we investigate the impact of tattoos on the performance of state-of-the-art hand recognition systems. To this end, we first propose a method for generating semi-synthetic tattooed hands. A benchmark is then performed for tattooed and non-tattooed hands. Experimental results computed on a freely available database showed that, although in some cases the use of tattoos assists hand recognition, the observed trend is a deterioration of recognition accuracy, indicating the sensitivity of hand recognition systems to tattoos.
  • 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.