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P296 - BIOSIG 2019 - Proceedings of the 18th International Conference of the Biometrics Special Interest Group

https://dl.gi.de/handle/20.500.12116/34220
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Auflistung P296 - BIOSIG 2019 - Proceedings of the 18th International Conference of the Biometrics Special Interest Group nach Schlagwort "Authentication"

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  • Konferenzbeitrag
    Android Pattern Unlock Authentication - effectiveness of local and global dynamic features
    (BIOSIG 2019 - Proceedings of the 18th International Conference of the Biometrics Special Interest Group, 2019) Ibrahim, Nasiru; Sellahewa, Harin
    This study conducts a holistic analysis of the performances of biometric features incorporated into Pattern Unlock authentication. The objective is to enhance the strength of the authentication by adding an implicit layer. Earlier studies have incorporated either global or local dynamic features for verification; however, as found in this paper, different features have variable discriminating power, especially at different extraction levels. The discriminating potential of global, local and their combination are evaluated. Results showed that locally extracted features have higher discriminating power than global features and combining both features gives the best verification performance. Further, a novel feature was proposed and evaluated, which was found to have a varied impact (both positive and negative) on the system performance. From our findings, it is essential to evaluate features (independently and collectively), extracted at different levels (global and local) and different combination for some might impede on the verification performance of the system.
  • Konferenzbeitrag
    Multi-resolution Local Descriptor for 3D Ear Recognition
    (BIOSIG 2019 - Proceedings of the 18th International Conference of the Biometrics Special Interest Group, 2019) Ganapathi, Iyyakutti Iyappan; Ali, Syed Sadaf; Prakash, Surya
    Several approaches have shown promising results in human ear recognition. However, factors such as the pose, illumination, and scaling have an enormous impact on recognition performance. 3D models are insensitive to these factors and are found to be better at enhancing recognition performance with strong geometric information. Low cost 3D data acquisition has also boosted the research community in recent times to explore more about 3D object recognition. We present a local multi-resolution descriptor in this paper to recognize human ears in 3D. For each key-point in 3D ear, a local reference frame (LRF) is constructed. Using multi-radii, we find neighbors at each key-point and the neighbors obtained from each radius are projected to create a depth image using the LRF. Further, a descriptor is computed by employing neural network based auto-encoders and the local statistics of the depth images. The descriptor is used to locate the potential correspondence matching points in the probe and gallery images for a coarse arrangement, followed by a fine alignment to compute the registration error. The obtained registration error is used as the matching score. The proposed technique is assessed on UND-J2 dataset to demonstrate its effectiveness.