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P339 - BIOSIG 2023 - Proceedings of the 22nd International Conference of the Biometrics Special Interest Group

https://dl.gi.de/handle/20.500.12116/43257
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Auflistung P339 - BIOSIG 2023 - Proceedings of the 22nd International Conference of the Biometrics Special Interest Group nach Schlagwort "Biometric and multimedia forensics"

1 - 3 von 3
  • Konferenzbeitrag
    Cyclist Recognition from a Silhouette Set
    (BIOSIG 2023, 2023) Eijiro Makishima, Fumito Shinmura
    Person recognition from surveillance cameras can be useful for criminal investigations. Currently, gait recognition technology can identify walking individuals, but recognition of people riding bicycles has not been actively investigated, despite cycling being a popular mode of transportation. In this paper, we propose a method to recognize individuals riding bicycles (cyclists) using a silhouette set. We captured two types of cyclist data, normal and rush modes, from five different views, and generated silhouette image sequences from this data. We evaluated accuracy of the proposed method on the silhouette images in identification and verification tasks. The evaluation results demonstrate the effectiveness 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
    Synthetic Latent Fingerprint Generation Using Style Transfer
    (BIOSIG 2023, 2023) Amol S Joshi, Ali Dabouei
    Limited data availability is a challenging problem in the latent fingerprint domain. Synthetically generated fingerprints are vital for training data-hungry neural network-based algorithms. Conventional methods distort clean fingerprints to generate synthetic latent fingerprints. We propose a simple and effective approach using style transfer and image blending to synthesize realistic latent fingerprints. Our evaluation criteria and experiments demonstrate that the generated synthetic latent fingerprints preserve the identity information from the input contact-based fingerprints while possessing similar characteristics as real latent fingerprints. Additionally, we show that the generated fingerprints exhibit several qualities and styles, suggesting that the proposed method can generate multiple samples from a single fingerprint.