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Auflistung nach Schlagwort "Biometrics"

1 - 10 von 24
  • Konferenzbeitrag
    3D Face Recognition For Cows
    (BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Yeleshetty, Deepak; Spreeuwers, Luuk; Li, Yan
    This paper presents a method to recognize cows using their 3D face point clouds. Face is chosen because of the rigid structure of the skull compared to other parts. The 3D face point clouds are acquired using a newly designed dual 3D camera setup. After registering the 3D faces to a specific pose, the cow’s ID is determined by running Iterative Closest Point (ICP) method on the probe against all the point clouds in the gallery. The root mean square error (RMSE) between the ICP correspondences is used to identify the cows. The smaller the RMSE, the more likely that the cow is from the same class. In a closed set of 32 cows with 5 point clouds per cow in the gallery, the ICP recognition demonstrates an almost perfect identification rate of 99.53%.
  • 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
    Benefits of Gaussian Convolution in Gait Recognition
    (BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Marsico, Maria De; Mecca, Alessio
    The first and still popular approach to gait recognition applies computer vision techniques to appearance-based features of walking patterns. More recently, wearable sensors have become attractive. The accelerometer is the most used one, being embedded in widespread mobile devices. Related techniques do not suffer for problems like occlusion and point of view, but for intra-subject variations caused by walking speed, ground type, shoes, etc. However, we can often recognize a person from the walking pattern, and this stimulates to search for robust features, able to sufficiently characterize this trait. This paper presents some preliminary experiments using the convolution with Gaussian kernels to extract relevant gait elements. The experiments use the large ZJU-gaitacc public dataset, and achieve improved results compared with previous works exploiting the same dataset.
  • Textdokument
    Biometric Systems under Morphing Attacks: Assessment of Morphing Techniques and Vulnerability Reporting
    (BIOSIG 2017, 2017) Scherhag,Ulrich; Nautsch,Andreas; Rathgeb,Christian; Gomez-Barrero,Marta; Veldhuis,Raymond N.J.; Spreeuwers,Luuk; Schils,Maikel; Maltoni,Davide; Grother,Patrick; Marcel,Sébastien; Breithaupt,Ralph; Ramachandra,Raghavendra; Busch,Christoph
    With the widespread deployment of biometric recognition systems, the interest in attacking these systems is increasing. One of the easiest ways to circumvent a biometric recognition system are so-called presentation attacks, in which artefacts are presented to the sensor to either impersonate another subject or avoid being recognised. In the recent past, the vulnerabilities of biometric systems to so-called morphing attacks have been unveiled. In such attacks, biometric samples of multiple subjects are merged in the signal or feature domain, in order to allow a successful verification of all contributing subjects against the morphed identity. Being a recent area of research, there is to date no standardised manner to evaluate the vulnerability of biometric systems to these attacks. Hence, it is not yet possible to establish a common benchmark between different morph detection algorithms. In this paper, we tackle this issue proposing new metrics for vulnerability reporting, which build upon our joint experience in researching this challenging attack scenario. In addition, recommendations on the assessment of morphing techniques and morphing detection metrics are given.
  • Konferenzbeitrag
    Evaluating Face Image Quality Score Fusion for Modern Deep Learning Models
    (BIOSIG 2022, 2022) Schlett, Torsten; Rathgeb, Christian; Tapia, Juan E.; Busch, Christoph
    Face image quality assessment algorithms attempt to estimate the utility of face images for biometric systems, typically face recognition, since the performance of these systems can be limited by the image quality. Hand-crafted quality score fusion has previously been examined for a variety of mostly factor-specific quality assessment algorithms. This paper instead examines score fusion for various recent “monolithic” quality assessment deep learning models. The evaluation methodology is based on Error-versus-Reject-Characteristic partial-Area-Under-Curve values, which are used to quantitatively rank quality assessment configurations in a face recognition context. Mean quality score fusion configurations were found to slightly improve performance on the TinyFace database, while the tested fusion types were ineffective on the LFW database.
  • Konferenzbeitrag
    Explaining ECG Biometrics: Is It All In The QRS?
    (BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Pinto, João Ribeiro; Cardoso, Jaime S.
    The literature seems to indicate that the QRS complex is the most important component of the electrocardiogram (ECG) for biometrics. To verify this claim, we use interpretability tools to explain how a convolutional neural network uses ECG signals to identify people, using on-theperson (PTB) and off-the-person (UofTDB) signals. While the QRS complex appears indeed to be a key feature on ECG biometrics, especially with cleaner signals, results indicate that, for larger populations in off-the-person settings, the QRS shares relevance with other heartbeat components, which it is essential to locate. These insights indicate that avoiding excessive focus on the QRS complex, using decision explanations during training, could be useful for model regularisation.
  • Konferenzbeitrag
    Face Presentation Attack Detection in Ultraviolet Spectrum via Local and Global Features
    (BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Siegmund, Dirk; Kerckhoff, Florian; Magdaleno, Javier Yeste; Jansen, V; Kirchbuchner, Florian; Kuijper, Arjan
    The security of the commonly used face recognition algorithms is often doubted, as they appear vulnerable to so-called presentation attacks. While there are a number of detection methods that are using different light spectra to detect these attacks this is the first work to explore skin properties using the ultraviolet spectrum. Our multi-sensor approach consists of learning features that appear in the comparison of two images, one in the visible and one in the ultraviolet spectrum. We use brightness and keypoints as features for training, experimenting with different learning strategies. We present the results of our evaluation on our novel Face UV PAD database. The results of our method are evaluated in an leave-one-out comparison, where we achieved an APCER/BPCER of 0%/0.2%. The results obtained indicate that UV images in presentation attack detection include useful information that are not easy to overcome.
  • Konferenzbeitrag
    Image Quality Assessment on Identity Documents
    (BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021) Yáñez, Claudio; Tapia, Juan
    This paper developed a method for performing Image Quality Assessment (IQA) on ID-Card images. First, we build the dataset, consisting of 204 images from Chilean ID-Cards, containing real and tampered images with varying quality levels. Then, we evaluated different features, obtaining the best results using the BRISQUE features and a newly trained SVR, with an $R^2$ score of 0.5868. This proposed method is called BRISQUE-ID. The IQA on ID-Cards can be used as a pre-processing stage for discarding lousy quality images and helping the subsequent steps in the processing pipeline.
  • Textdokument
    Improvement of Iris Recognition based on Iris-Code Bit-Error Pattern Analysis
    (BIOSIG 2017, 2017) Rathgeb,Christian; Busch,Christoph
    In this paper an advanced iris-biometric comparator is presented. In the proposed scheme an analysis of bit-error patterns produced by Hamming distance-based iris-code comparisons is performed. The lengths of sequences of horizontal consecutive mis-matching bits are measured and a frequency distribution is estimated. The difference of the extracted frequency distribution to that of an average genuine one obtained from a training set is used as a second comparison score. This score is then used together with the fractional Hamming distance in order to improve the recognition accuracy of an iris recognition system. In experimental evaluations relative improvements of approximately 45% and 10% in terms of false non-match rate at a false match rate of 0.01% are achieved on the CASIAv4-Interval and the BioSecure iris databases, respectively.
  • Konferenzbeitrag
    On the assessment of face image quality based on handcrafted features
    (BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Henniger, Olaf; Fu, Biying; Chen, Cong
    This paper studies the assessment of the quality of face images, predicting the utility of face images for automated recognition. The utility of frontal face images from a publicly available dataset was assessed by comparing them with each other using commercial off-the-shelf face recognition systems. Multiple face image features delineating face symmetry and characteristics of the capture process were analysed to find features predictive of utility. The selected features were used to build system-specific and generic random forest classifiers.