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Auflistung nach Autor:in "Dantcheva, Antitza"

1 - 10 von 122
  • 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
    Action-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, Hugo
    There 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.
  • Konferenzbeitrag
    Advanced Face Presentation Attack Detection on Light Field Database
    (BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Chiesa, Valeria; Dugelay, Jean-Luc
    In the last years several works have been focused on the impact of new sensors on face recognition. A particular interest has been addressed to technologies able to detect the depth of the scene as light field cameras. Together with person identification algorithms, new anti-spoofing methods customized for specific devices have to be investigated. In this paper, a new algorithm for presentation attack detection on light field face database is proposed. While distance between subject and camera is not a relevant information for standard 2D spoofing attacks, it could be important when using 3D cameras. We prove through three experiments that the proposed method based on depth map elaboration outperforms the existent algorithms in presentation attack detection on light field images.
  • Konferenzbeitrag
    Adversarial learning for a robust iris presentation attack detection method against unseen attack presentations
    (BIOSIG 2019 - Proceedings of the 18th International Conference of the Biometrics Special Interest Group, 2019) Ferreira, Pedro M.; Sequeira, Ana F.; Pernes, Diogo; Rebelo, Ana; Cardoso, Jaime S.
    Despite the high performance of current presentation attack detection (PAD) methods, the robustness to unseen attacks is still an under addressed challenge. This work approaches the problem by enforcing the learning of the bona fide presentations while making the model less dependent on the presentation attack instrument species (PAIS). The proposed model comprises an encoder, mapping from input features to latent representations, and two classifiers operating on these underlying representations: (i) the task-classifier, for predicting the class labels (as bona fide or attack); and (ii) the species-classifier, for predicting the PAIS. In the learning stage, the encoder is trained to help the task-classifier while trying to fool the species-classifier. Plus, an additional training objective enforcing the similarity of the latent distributions of different species is added leading to a ‘PAIspecies’- independent model. The experimental results demonstrated that the proposed regularisation strategies equipped the neural network with increased PAD robustness. The adversarial model obtained better loss and accuracy as well as improved error rates in the detection of attack and bona fide presentations.
  • 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
    Anomalies in measuring speed and other dynamic properties with touchscreens and tablets
    (BIOSIG 2019 - Proceedings of the 18th International Conference of the Biometrics Special Interest Group, 2019) Griechisch, Erika; Ward, Jean Renard; Hanczár, Gergely
    Touchscreens and tablets are often used in different studies and applications to capture high-resolution drawing, handwriting, or signatures. Several studies tend to analyse different properties, such as peaks or changes of the time derivatives of the coordinates; like velocity, angular velocity, acceleration or jerk of the movements. These are substantial features to analyse drawing, analyse or recognize handwriting, to examine the fluency of handwriting or verify signatures. The reliability of such a study strongly depends on the fidelity of the acquired data. We have tested several touchscreens and tablets which are widely used in different research studies, focusing on the resolution and accuracy of the coordinates and the uniformity of sampling. We have found that the vendors’ performance specifications (to the extent the vendor gives meaningful specifications) may seriously deviate from reality. Even if some of the raw data may look satisfactory at first sight, our examination uncovered several potentially significant bad behaviors, and instances in which the specifications from the vendors are, at best, misleading and incompletely informative. Some authors mention that the reliability of tablet data is unclear [Ha13, Fr05], but researchers may underestimate to what extent it could influence their results. This paper uncovers some aspects of the unreliability of the data and emphasizes the importance of understanding and addressing (or at least, knowing) the revealed problems prior to any analysis.
  • Konferenzbeitrag
    Application of affine-based reconstruction to retinal point patterns
    (BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Sadeghpour, Mahshid; Arakala, Arathi; Davis, Stephen A.; Horadam, Kathy J.
    Inverse biometrics that exploit the information of biometric references from comparison scores can compromise sensitive personal information of the users in biometric recognition systems. One inverse biometric method that has been very successful in regenerating face images applies an affine transformation to model the face recognition algorithm. This method is general and could apply to templates extracted from other biometric characteristics. This research proposes two formats to apply this method to spatial point patterns extracted from retina images and tests its performance on reconstructing such sparse templates. The results show that the quality of the reconstructed retina point pattern templates is lower than would be accepted by the system as mated.
  • Konferenzbeitrag
    Assessment of Sensor Ageing-Impact in Air Travelled Fingerprint Capturing Devices
    (BIOSIG 2021 - Proceedings of the 20th International Conference of the Biometrics Special Interest Group, 2021) Kauba, Christof; Kirchgasser, Simon; Jöchl, Robert; Uhl, Andreas
    Biometric recognition performance is affected by many factors, like varying acquisition conditions or ageing related effects, commonly denoted as biometric template ageing. Image sensor ageing, being part of biometric template ageing and a sub-field of image and video forensics, leads to defective pixels due to cosmic radiation, depending on the altitude. So far, image sensor ageing has only been a peripheral target in fingerprint research. We investigate the impact of image sensor ageing on various fingerprint capturing devices, including optical, capacitive and thermal ones. We established a fingerprint ageing dataset utilising 10 capturing devices which travelled on an air-plane for 127 days (to increase the number of developed defects). By evaluating the samples captured prior to their travel and afterwards using several state-of-the-art fingerprint quality metrics as well as minutiae-based fingerprint recognition systems we quantify the effect of image sensor ageing on fingerprint recognition. Furthermore, by employing a defect detection technique we quantify the number of defects developed during that period.
  • Konferenzbeitrag
    A benchmark database of visible and thermal paired face images across multiple variations
    (BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group, 2018) Mallat, Khawla; Dugelay, Jean-Luc
    Although visible face recognition systems have grown as a major area of research, they are still facing serious challenges when operating in uncontrolled environments. In attempt to overcome these limitations, thermal imagery has been investigated as a promising direction to extend face recognition technology. However, the reduced number of databases acquired in thermal spectrum limits its exploration. In this paper, we introduce a database of face images acquired simultaneously in visible and thermal spectra under various variations: illumination, expression, pose and occlusion. Then, we present a comparative study of face recognition performances on both modalities against each variation and the impact of bimodal fusion. We prove that thermal spectrum rivals with the visible spectrum not only in the presence of illumination changes, but also in case of expression and poses changes.
  • 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.