Andreas Shahverdyan

Bio: Andreas Shahverdyan is an academic researcher. The author has contributed to research in topics: Biometrics & Identification (information). The author has an hindex of 2, co-authored 2 publications receiving 28 citations.

An Overview on Multi-biometric Score-level Fusion - Verification and Identification

Abstract: Multi-biometrics is the use of multiple biometric recognition sources to provide a more dependable verification or identification decision. Fusion of multi-biometric sources can be performed on different levels, such as the data, feature, or score level. This work presents an overview of the multi-biometric score-level fusion problem, along with the proposed solution in the literature. A discussion is made to provide a comparison between multi-biometric fusion in both scenarios. This discussion aims at providing a clearer view of future developments especially under the identification scenario where many related applications are rapidly growing such as forensics and ubiquitous surveillance.

A Multi-detector Solution Towards an Accurate and Generalized Detection of Face Morphing Attacks

Abstract: Face morphing attack images are built to be verifiable to multiple identities. Associating such images to identity documents leads to building faulty identity links, causing vulnerabilities in security critical processes. Recent works have studied the face morphing attack detection performance over variations in morphing approaches, pointing out low generalization. This work introduces a multi-detector fusion solution that aims at gaining both, accuracy and generalization over different morphing types. This is performed by fusing classification scores produced by detectors trained on databases with variations in morphing type and image pairing protocols. This work develop and evaluate the proposed solution along with baseline solutions by building a database with three different pairing protocols and two different morphing approaches. This proposed solution successfully lead to decreasing the Bona Fide Presentation Classification Error Rate at 1.0% Attack Presentation Classification Error Rate from 15.7% and 3.0% of the best performing single detector to 2.7% and 0.0%, respectively on two face morphing techniques, pointing out a highly generalized performance.

Performance Anchored Score Normalization for Multi-biometric Fusion

Abstract: This work presents a family of novel normalization techniques for score-level multi-biometric fusion. The proposed normalization is not only concerned to bring comparison scores to a common range and scale, it also focuses in bringing certain operational performance points in the distribution into alignment. The Performance Anchored Normalization (PAN) algorithms discussed here were tested on the extended Multi Modal Verification for Teleservices and Security applications database (XM2VTS) and proved to outperform conventional score normalization techniques in most tests. The tests were performed with combination fusion rules and presented as biometric verification performance measures.

Deep Learning Multi-layer Fusion for an Accurate Iris Presentation Attack Detection

Abstract: Iris presentation attack detection (PAD) algorithms are developed to address the vulnerability of iris recognition systems to presentation attacks. Taking into account that the deep features successfully improved computer vision performance in various fields including iris recognition, it is natural to use features extracted from deep neural networks for iris PAD. Each layer in a deep learning network carries features of different level of abstraction. The features extracted from the first layer to the higher layers become more complex and more abstract. This might point our complementary information in these features that can collaborate towards an accurate PAD decision. Therefore, we propose an iris PAD solution based on multi-layer fusion. The information extracted from the last several convolutional layers are fused on two levels, feature-level and score-level. We demonstrated experiments on both, off-the-shelf pre-trained network and network trained from scratch. An extensive experiment also explores the complementary between different layer combinations of deep features. Our experimental results show that feature-level based multi-layer fusion method performs better than the best single layer feature extractor in most cases. In addition, our fusion results achieve similar or better results than the state-of-the-art algorithms on the Notre Dame and IIITD-WVU databases of the Iris Liveness Detection Competition 2017 (LivDet-Iris 2017).

Multi-biometric Score-Level Fusion and the Integration of the Neighbors Distance Ratio

Abstract: Multi-biometrics aims at building more accurate unified bio-metric decisions based on the information provided by multiple biometric sources. Information fusion is used to optimize the process of creating this unified decision. In previous works dealing with score-level multi-biometric fusion, the scores of different biometric sources belonging to the comparison of interest are used to create the fused score. The novelty of this work focuses on integrating the relation of the fused scores to other comparisons within a 1:N comparison. This is performed by considering the neighbors distance ratio in the ranked comparisons set within a classification-based fusion approach. The evaluation was performed on the Biometric Scores Set BSSR1 database and the enhanced performance induced by the integration of neighbors distance ratio was clearly presented.

Missing data estimation in multi-biometric identification and verification

Abstract: In the practical use of multi-biometric solutions, biometric sources involved in producing the verification or identification decision do occasionally fail to produce results. This work discusses solutions for missing data in multi-biometric score-level fusion. A missing data estimation solution based on support vector regression was presented in this work and compared to four baseline solutions. The evaluation was carried under both the verification and the identification scenarios in an effort to show the effect of missing data estimation on the relatively understudied multi-biometric identification scenario. Evaluation was performed on the Biosecure DS2 score database and satisfying performance was achieved under both biometric scenarios.