P306 - BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group
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- KonferenzbeitragEffects of sample stretching in face recognition(BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Hedberg, Mathias FredrikFace stretching is something that can occur intentionally and unintentionally when preparing a face sample for enrollment in a face recognition system. In this paper we assess what affects both horizontal and vertical stretching have on a face recognition algorithms. Basic closed-set identification tests revealed that holistic face recognition algorithms performed poorly compared to feature based recognition algorithms when classifying non-stretched samples against templates based on stretched samples.
- KonferenzbeitragEnd-to-end Off-angle Iris Recognition Using CNN Based Iris Segmentation(BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Jalilian, Ehsaneddin; Karakaya, Mahmut; Uhl, AndreasWhile deep learning techniques are increasingly becoming a tool of choice for iris segmentation, yet there is no comprehensive recognition framework dedicated for off-angle iris recognition using such modules. In this work, we investigate the effect of different gaze-angles on the CNN based off-angle iris segmentations, and their recognition performance, introducing an improvement scheme to compensate for some segmentation degradations caused by the off-angle distortions. Also, we propose an off-angle parameterization algorithm to re-project the off-angle images back to frontal view. Taking benefit of these, we further investigate if: (i) improving the segmentation outputs and/or correcting the iris images before or after the segmentation, can compensate for off-angle distortions, or (ii) the generalization capability of the network can be improved, by training it on iris images of different gaze-angles. In each experimental step, segmentation accuracy and the recognition performance are evaluated, and the results are analyzed and compared.
- KonferenzbeitragMinutiae-based Finger Vein Recognition Evaluated with Fingerprint Comparison Software(BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Castillo-Rosado, Katy; Linortner, Michael; Uhl, Andreas; Mendez-Vasquez, Heydi; Hernandez-Palancar, JoséFinger vein recognition is a biometric authentication technique based on the vein patterns of human fingers. Despite the fact that classical approaches are based on correlation, the topology of vein patterns allows the use of minutiae points for their representation. Minutiae points are the most used features for representing ridge patterns in fingerprints. In literature, it has been shown that minutiae can be used for finger vein comparison, but low image quality provokes that many spurious minutiae are extracted from them. In this work, a preprocessing method is presented, that combines classical digital image processing methods and level set theory in order to extract a set with the most reliable minutiae. The experiments were performed on two publicly available databases and different comparison methods were used for testing the representative character of the minutiae set extracted. The results showed that even though the amount of extracted minutiae is around 15-30, effective identification is possible.
- KonferenzbeitragEfficiency Analysis of Post-quantum-secure Face Template Protection Schemes based on Homomorphic Encryption(BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Kolberg, Jascha; Drozdowski, Pawel; Gomez-Barrero, Marta; Rathgeb, Christian; Busch, ChristophSince biometric characteristics are not revocable and biometric data is sensitive, privacypreserving methods are essential to operate a biometric recognition system. More precisely, the biometric information protection standard ISO/IEC IS 24745 requires that biometric templates are stored and compared in a secure domain. Using homomorphic encryption (HE), we can ensure permanent protection since mathematical operations on the ciphertexts directly correspond to those on the plaintexts. Thus, HE allows to compute the distance between two protected templates in the encrypted domain without a degradation of biometric performance with respect to the corresponding system. In this paper, we benchmark three post-quantum-secure HE schemes, and thereby show that a face verification in the encrypted domain requires only 50 ms transaction time and a template size of 5.5 KB.
- KonferenzbeitragFisher Vector Encoding of Dense-BSIF Features for Unknown Face Presentation Attack Detection(BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) González-Soler, Lázaro J.; Gomez-Barrero, Marta; Busch, ChristophThe task of determining whether a sample stems from a real subject (i.e, it is a bona fide presentation) or it comes from an artificial replica (i.e., it is an attack presentation) is a mandatory requirement for biometric capture devices, which has received a lot of attention in the recent past. Nowadays, most face Presentation Attack Detection (PAD) approaches have reported a good detection performance when they are evaluated on known Presentation Attack Instruments (PAIs) and acquisition conditions, in contrast to more challenging scenarios where unknown attacks are included in the evaluation. For those more realistic scenarios, the existing approaches are in many cases unable to detect unknown PAI species. In this work, we introduce a new feature space based on Fisher vectors, computed from compact Binarised Statistical Image Features (BSIF) histograms, which allows finding semantic feature subsets from known samples in order to enhance the detection of unknown attacks. This new representation, evaluated over three freely available facial databases, shows promising results in the top state-of-the-art: a BPCER100 under 17% together with a AUC over 98% can be achieved in the presence of unknown attacks.
- KonferenzbeitragChildFace: Gender Aware Child Face Aging(BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Chandaliya, Praveen Kumar; Sinha, Aditya; Nain, NeetaChild face aging and rejuvenation has amassed considerable active research interest due to its immense impact on monitoring applications especially for finding lost/abducted children with childhood photos and hence protect children. Prior studies are primarily motivated to enhance the generation quality and aging of face images, rather than quantifying face recognition performance. To address this challenge we propose ChildFace model. Our model does child face aging and rejuvenation while using gender as condition. Our model uses Conditional Generative Adversarial Nets (cGANs), VGG19 based perceptual loss and LightCNN29 age classifier and produces impressive results. Intense quantitative study based on verification, identification and age estimation proves that our model is competent to existing state-of-art models and can make a significant contribution in identifying missing children.
- KonferenzbeitragImproved Liveness Detection in Dorsal Hand Vein Videos using Photoplethysmography(BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Schuiki, Johannes; Uhl, AndreasIn this study, a previously published infrared finger vein liveness detection scheme is tested for its applicability on dorsal hand vein videos. A custom database consisting of five different types of presentation attacks recorded with transillumination as well as reflected light illumination is examined. Additionally, two different methods for liveness detection are presented in this work. All methods described employ the concept of generating a signal through the change in average pixel illumination, which is referred to as Photoplethysmography. Feature vectors in order to classify a given video sequence are generated using spectral analysis of the time series. Experimental results show the effectiveness of the proposed methods.
- KonferenzbeitragAction-Independent Generalized Behavioral Identity Descriptors for Look-alike Recognition in Videos(BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Khodabakhsh, Ali; Loiselle, HugoThere is a long history of exploitation of the visual similarity of look-alikes for fraud and deception. The visual similarity along with the application of physical and digital cosmetics greatly challenges the recognition ability of average humans. Face recognition systems are not an exception in this regard and are vulnerable to such similarities. In contrast to physiological face recognition, behavioral face recognition is often overlooked due to the outstanding success of the former. However, the behavior of a person can provide an additional source of discriminative information with regards to the identity of individuals when physiological attributes are not reliable. In this study, we propose a novel biometric recognition system based only on facial behavior for the differentiation of look-alikes in unconstrained recording conditions. To this end, we organized a dataset of 85;656 utterances from 1000 look-alike pairs based on videos collected from the wild, large enough for the development of deep learning solutions. Our selection criteria assert that for these collected videos, both state-of-the-art biometric systems and human judgment fail in recognition. Furthermore, to utilize the advantage of large-scale data, we introduce a novel action-independent biometric recognition system that was trained using triplet-loss to create generalized behavioral identity embeddings. We achieve look-alike recognition equal-error-rate of 7:93% with sole reliance on the behavior descriptors extracted from facial landmark movements. The proposed method can have applications in face recognition as well as presentation attack detection and Deepfake detection.
- KonferenzbeitragFacial Profiles Recognition Using Comparative Facial Soft Biometrics(BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Alamri, Malak; Mahmoodi, SasanThis study extends previous advances in soft biometrics and describes to what extent soft biometrics can be used for facial profile recognition. The purpose of this research is to explore human recognition based on facial profiles in a comparative setting based on soft biometrics. Moreover, in this work, we describe and use a ranking system to determine the recognition rate. The Elo rating system is employed to rank subjects by using their face profiles in a comparative setting. The crucial features responsible for providing useful information describing facial profiles have been identified by using relative methods. Experiments based on a subset of the XM2VTSDB database demonstrate a 96% for recognition rate using 33 features over 50 subjects.
- KonferenzbeitragTouchless Fingerprint Sample Quality: Prerequisites for the Applicability of NFIQ2.0(BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Priesnitz, Jannis; Rathgeb, Christian; Buchmann, Nicolas; Busch, ChristophThe impact of fingerprint sample quality on biometric performance is undisputed. For touch-based fingerprint data, the effectiveness of the NFIQ2.0 quality estimation method is well documented in scientific literature. Due to the increasing use of touchless fingerprint recognition systems a thorough investigation of the usefulness of the NFIQ2.0 for touchless fingerprint data is of interest. In this work, we investigate whether NFIQ2.0 quality scores are predictive of error rates associated with the biometric performance of touchless fingerprint recognition. For this purpose, we propose a touchless fingerprint preprocessing that favours NFIQ2.0 quality estimation which has been designed for touch-based fingerprint data. Comparisons are made between NFIQ2.0 score distributions obtained from touch-based and touchless fingerprint data of the publicly available FVC06, MCYT, PolyU, and ISPFDv1 databases. Further, the predictive power regarding biometric performance is evaluated in terms of Error-versus-Reject Curves (ERCs) using an open source fingerprint recognition system. Under constrained capture conditions NFIQ2.0 is found to be an effective tool for touchless fingerprint quality estimation if an adequate preprocessing is applied.