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P270 - BIOSIG 2017 - Proceedings of the 16th International Conference of the Biometrics Special Interest Group

https://dl.gi.de/handle/20.500.12116/20083
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Auflistung P270 - BIOSIG 2017 - Proceedings of the 16th International Conference of the Biometrics Special Interest Group nach Erscheinungsdatum

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  • Textdokument
    SIC-Gen: A Synthetic Iris-Code Generator
    (BIOSIG 2017, 2017) Drozdowski,Pawel; Rathgeb,Christian; Busch,Christoph
    Nowadays large-scale identity management systems enrol more than one billion data subjects. In order to limit transaction times, biometric indexing is a suitable method to reduce the search space in biometric identifications. Effective testing of such biometric identification systems and biometric indexing approaches requires large datasets of biometric data. Currently, the size of the publicly available iris datasets is insufficient, especially for system scalability assessments. Synthetic data generation offers a potential solution to this issue; however, it is challenging to generate data hat is both statistically sound and visually realistic - for the iris, the currently available approaches prove unsatisfactory. In this paper, we present a method for generation of synthetic binary iris-based templates, i.e. Iris-Codes, which are the de facto standard used throughout major biometric deployments around the world. We validate the statistical properties of the synthetic templates and show that they closely resemble ones produced from real ocular images. With the proposed approach, large databases of synthetic Iris-Codes with flexibly adjustable properties can be generated.
  • Textdokument
    Multi-scale facial scanning via spatial LSTM for latent facial feature representation
    (BIOSIG 2017, 2017) Kim,Seong Tae; Choi,Yeoreum; Ro,Yong Man
    In the past few decades, automatic face recognition has been an important vision task. In this paper, we exploit the spatial relationships of facial local regions by using a novel deep network. In the proposed method, face is spatially scanned with spatial long short-term memory (LSTM) to encode the spatial correlation of facial regions. Moreover, with facial regions of various scales, the complementary information of the multi-scale facial features is encoded. Experimental results on public database showed that the proposed method outperformed the conventional methods by improving the face recognition accuracy under illumination variation.
  • Textdokument
    Improving Very Low-Resolution Iris Identification Via Super-Resolution Reconstruction of Local Patches
    (BIOSIG 2017, 2017) Alonso-Fernandez,Fernando; Farrugia,Reuben A.; Bigun,Josef
    Relaxed acquisition conditions in iris recognition systems have significant effects on the quality and resolution of acquired images, which can severely affect performance if not addressed properly. Here, we evaluate two trained super-resolution algorithms in the context of iris identification. They are based on reconstruction of local image patches, where each patch is reconstructed separately using its own optimal reconstruction function. We employ a database of 1,872 near-infrared iris images (with 163 different identities for identification experiments) and three iris comparators. The trained approaches are substantially superior to bilinear or bicubic interpolations, with one of the comparators providing a Rank-1 performance of ∼88% with images of only 15×15 pixels, and an identification rate of 95% with a hit list size of only 8 identities.
  • Textdokument
    Evaluation of CNN architectures for gait recognition based on optical flow maps
    (BIOSIG 2017, 2017) Castro,Francisco M.; Marín-Jiménez,Manuel J.; Guil,Nicolás; López-Tapia,Santiago; de la Blanca,Nicolás Pérez
    This work targets people identification in video based on the way they walk (i.e.gait) by using deep learning architectures. We explore the use of convolutional neural networks (CNN) for learning high-level descriptors from low-level motion features (i.e.optical flow components). The low number of training samples for each subject and the use of a test set containing subjects different from the training ones makes the search of a good CNN architecture a challenging task.We carry out a thorough experimental evaluation deploying and analyzing four distinct CNN models with different depth but similar complexity. We show that even the simplest CNN models greatly improve the results using shallow classifiers. All our experiments have been carried out on the challenging TUMGAID dataset, which contains people in different covariate scenarios (i.e.clothing, shoes, bags).
  • Textdokument
    Multimodal Neural Network for Overhead Person Re-identification
    (BIOSIG 2017, 2017) Lejbølle, Aske R.; Nasrollahi, Kamal; Krogh, Benjamin; Moeslund,Thomas B.
    Person re-identification is a topic which has potential to be used for applications within forensics, flow analysis and queue monitoring. It is the process of matching persons across two or more camera views, most often by extracting colour and texture based hand-crafted features, to identify similar persons. Because of challenges regarding changes in lighting between views, occlusion or even privacy issues, more focus has turned to overhead and depth based camera solutions. Therefore, we have developed a system, based on a Convolutional Neural Network (CNN) which is trained using both depth and RGB modalities to provide a fused feature. By training on a locally collected dataset, we achieve a rank-1 accuracy of 74.69%, increased by 16.00% compared to using a single modality. Furthermore, tests on two similar publicly available benchmark datasets of TVPR and DPI-T show accuracies of 77.66% and 90.36%, respectively, outperforming state-of-the-art results by 3.60% and 5.20%, respectively.
  • Textdokument
    Fusing Biometric Scores using Subjective Logic for Gait Recognition on Smartphone
    (BIOSIG 2017, 2017) Wasnik,Pankaj; Schäfer,Kirstina; Raja,Kiran; Ramachandra,Raghavendra; Busch,Christoph
    The performance of a biometric system gets affected by various types of errors such as systematic errors, random errors, etc. These kinds of errors usually occur due to the natural variations in the biometric traits of subjects, different testing, and comparison methodologies. Neither of these errors can be easily quantifiable by mathematical formulas. This behavior introduces an uncertainty in the biometric verification or identification scores. The combination of comparison scores from different comparators or combination of multiple biometric modalities could be a better approach for improving the overall recognition performance of a biometric system. In this paper, we propose a method for combining such scores from multiple comparators using Subjective Logic (SL), as it takes uncertainty into account while performing to biometric fusion. This paper proposes a framework for a smartphone based gait recognition system with application of SL for biometric data fusion.
  • Textdokument
    Unobtrusive Gait Recognition using Smartwatches
    (BIOSIG 2017, 2017) Al-Naffakh,Neamah; Clarke,Nathan; Li,Fudong; Haskell-Dowland,Paul
    Gait recognition is a technique that identifies or verifies people based upon their walking patterns. Smartwatches, which contain an accelerometer and gyroscope have recently been used to implement gait-based biometrics. However, this prior work relied upon data from single sessions for both training and testing, which is not realistic and can lead to overly optimistic performance results. This paper aims to remedy some of these problems by training and evaluating a smartwatch-based biometric system on data obtained from different days. Also, it proposes an advanced feature selection approach to identify optimal features for each user. Two experiments are presented under three different scenarios: Same-Day, Mixed-Day, and Cross-Day. Competitive results were achieved (best EERs of 0.13% and 3.12% by using the Same day data for accelerometer and gyroscope respectively and 0.69% and 7.97% for the same sensors under the Cross-Day evaluation. The results show that the technology is sufficiently capable and the signals captured sufficiently discriminative to be useful in performing gait recognition.
  • Textdokument
    How Random is a Classifier given its Area under Curve?
    (BIOSIG 2017, 2017) Zeinstra,Chris; Veldhuis,Raymond; Spreeuwers,Luuk
    When the performance of a classifier is empirically evaluated, the Area Under Curve (AUC) is commonly used as a one dimensional performance measure. In general, the focus is on good performance (AUC towards 1). In this paper, we study the other side of the performance spectrum (AUC towards 0.50) as we are interested to which extend a classifier is random given its AUC. We present the exact probability distribution of the AUC of a truely random classifier, given a finite number of distinct genuine and imposter scores. It quantifies the “randomness” of the measured AUC. The distribution involves the restricted partition function, a well studied function in number theory. Although other work exists that considers confidence bounds on the AUC, the novelty is that we do not assume any underlying parametric or non-parametric model or specify an error rate. Also, in cases in which a limited number of scores is available, for example in forensic case work, the exact distribution can deviate from these models. For completeness, we also present an approximation using a normal distribution and confidence bounds on the AUC.
  • Textdokument
    Fingerprint Template Ageing vs. Template Changes Revisited
    (BIOSIG 2017, 2017) Kirchgasser,Simon; Uhl,Andreas
    This study investigates the impact of “ghost” fingerprint and minutiae information in 4 year time-span separated fingerprint datasets. A high amount of ghost fingerprints within the data, eventually a source for differences in acquisition conditions, might be responsible for recently reported template ageing effects. According to that, various experiments have been performed to get rid of this problematic image content and to compare the corresponding matching results to the performance figures using the non altered imprints. The analysis with respect to detected increased error rates exhibits very similar effects for all considered methods no matter if ghost fingerprint information is removed or not. Thus, ghost fingerprints are not responsible for the observed effects.
  • 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.