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- Konferenzbeitrag2D Face Liveness Detection: an Overview(BIOSIG 2012, 2012) Kähm, Olga; Damer, NaserFace recognition based on 2D images is a widely used biometric approach. This is mainly due to the simplicity and high usability of this approach. Nonetheless, those solutions are vulnerable to spoof attacks made by non-real faces. In order to identify malicious attacks on such biometric systems, 2D face liveness detection approaches are developed. In this work, face liveness detection approaches are categorized based on the type of liveness indicator used. This categorization helps understanding different spoof attacks scenarios and their relation to the developed solutions. A review of the latest works dealing with face liveness detection works is presented. A discussion is made to link the state of the art solutions with the presented categorization along with the available and possible future datasets. All that aim to provide a clear path for the future development of innovative face liveness detection solutions.
- Konferenzbeitrag3D capturing of fingerprints – on the way to a contactless certified sensor(BIOSIG 2011 – Proceedings of the Biometrics Special Interest Group, 2011) Koller, Dieter; Walchshäusl, Leonard; Eggers, Georg; Neudel, Frank; Kursawe, Ulrich; Kühmstedt, Peter; Heinze, Matthias; Ramm, Roland; Bräuer-Burchard, Christian; Notni, Gunther; Kafka, Ricarda; Neubert, Ralf; Seibert, Helmut; Castro-Neves, Margarida; Nouak, AlexanderThe purpose of this paper is to describe the development and performance tests of a contact-free fingerprint sensor, TrueFinger3D (TF3D). This contactless fingerprint sensor is designed to be perfectly interoperable with fingerprint image data captured with contact-based sensors or ink pads. This is achieved by acquiring a 3D dataset of the fingertip together with the image of the papillary lines. Based on the 3D data, the papillary lines image can be processed to compensate perspective foreshortening or even emulate deformation effects caused with contact-based sensors. The 3D measurement mechanism and the image processing are described in detail. The resulting fingerprint images taken by the contactless sensor are then matched with images taken by regular contact-based fingerprint readers at different force levels. The comparison shows that the geometric distortion of our contactless sensor TF3D is comparable to that of contact-based sensors deployed under regular conditions. Our test also shows that contact-based sensors operated under irregular or strong force conditions suffer from a substantial performance degradation, not seen with the contactless sensor TF3D, which has perfect reproducibility. The results also indicate perfect interoperability of the TF3D with any contact-based data and should therefore entitle the sensor to a certification for governmental use.
- Konferenzbeitrag3D Face Recognition For Cows(BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Yeleshetty, Deepak; Spreeuwers, Luuk; Li, YanThis 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%.
- Konferenzbeitrag3D face recognition in the presence of 3D model degradations(BIOSIG 2011 – Proceedings of the Biometrics Special Interest Group, 2011) Lemaire, Pierre; Huang, Di; Colineau, Joseph; Ardabilian, Mohsen; Chen, LimingThe problem of 3D face recognition has received a growing interest in the past decades. While proposed approaches have proven their efficiency over renowned databases as FRGC, little work has been conducted on the robustness of such algorithm to the quality of 3D models. In this work, we present a study of the robustness of our 3D face recognition algorithm, namely MS-ELBP+SIFT, to face model degradations. Those degradations include Gaussian noise, decimation, and holes. Degradations are generated on a subset of the FRGC database, hence enabling us to compare the robustness of our approach to them. Results are provided through a comparative study with the baseline ICP method.
- Konferenzbeitrag3D face recognition on low-cost depth sensors(BIOSIG 2014, 2014) Mráček, Štěpán; Drahanský, Martin; Dvořák, Radim; Provazník, Ivo; Váňa, JanThis paper deals with the biometric recognition of 3D faces with the emphasis on the low-cost depth sensors; such are Microsoft Kinect and SoftKinetic DS325. The presented approach is based on the score-level fusion of individual recognition units. Each unit processes the input face mesh and produces a curvature, depth, or texture representation. This image representation is further processed by specific Gabor or Gauss-Laguerre complex filter. The absolute response is then projected to lowerdimension representations and the feature vector is thus extracted. Comparison scores of individual recognition units are combined using transformation-based, classifierbased, or density-based score-level fusion. The results suggest that even poor quality low-resolution scans containing holes and noise might be successfully used for recognition in relatively small databases.
- Editiertes Buch3D measurement of human faces for biometric application by digital fringe projection with digital light projection (DLP)(BIOSIG 2008: Biometrics and Electronic Signatures, 2008) Benderoth, Christian; Yan, Jun; Hooshiar, Kavon; Bell, Rebecca L.; Frankowski, GottfriedFacial recognition of people can be used for the identification of individuals, or can serve as verification e.g. for access controls. The process requires that the facial data is captured and then compared with stored reference data. In order to obtain better identification and verification performance and to avoid a number of security weaknesses, 3-dimensional facial recognition systems can be used, which outperform in both cases the 2-dimensional systems that are currently used. In this context, an optical 3D face scanning system with structured light projection based on DMD will be introduced, and the performance of the system will be analysed.
- Konferenzbeitrag3D whole hand targets: evaluating slap and contactless fingerprint readers(Biosig 2016, 2016) Arora, Sunpreet S.; Jain, Anil K.; Jr., Nicholas G. Paulter
- KonferenzbeitragAbsolute fingerprint pre-alignment in minutiae-based cryptosystems(BIOSIG 2013, 2013) Tams, BenjaminMost biometric cryptosystems that have been proposed to protect fingerprint minutiae make use of public alignment helper data. This, however, has the inadvertent effect of information leakage about the protected templates. A countermeasure to avoid auxiliary alignment data is to protect absolutely pre-aligned fingerprints. As a proof of concept, we run performance evaluations of a minutiae fuzzy vault with an automatic method for absolute pre-alignment. Therefore, we propose a new method for estimating a fingerprint's directed reference point by modeling the local orientation around the core as a tented arch.1
- 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.
- KonferenzbeitragActivity related biometrics based on motion trajectories(BIOSIG 2010: Biometrics and Electronic Signatures. Proceedings of the Special Interest Group on Biometrics and Electronic Signatures, 2010) Drosou, Anastasios; Moustakas, Konstantinos; Ioannidis, Dimos; Tzovaras, DimitriosThe current paper contributes to the concept of activity-related biometric authentication in ambient Intelligence environments. The motivation behind the proposed approach derives from activity-related biometrics and is mainly focusing on everyday activities. The activity sequence is captured by a stereoscopic camera and the resulting 2.5D data are processed to extract valuable unobtrusive activity-related features. The novel contribution of the current work lies in the warping of the extracted movements trajectories, so as to compensate for different environmental settings. Au- thentication is performed utilizing both HMM and GMMs. The authentication results performed on a database with 32 subjects show that the current work outperforms existing approaches especially in the case of non-interaction restricting scenarios.