Iurii received his bachelor degree in radiophysics in Peter the Great Saint Petersburg Polytechnic University, Saint-Petersburg (Russia 2015). During his following study he defended M.Sc thesis devoted to the development of microwave decoupling devices. He moved to Portugal in 2017 to take part in a project related to the investigation of metamaterial-based superabsorbers, which have a potential in wireless energy transfer. Currently, he is collaborating as a researcher in TrustFaces project.
Projects
TrustFaces
The TrustFaces project derives from the TrustStamp project, completed in June 2018, in partnership w...
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FACING
Os principais objetivos deste projeto são a realização de baterias exaustivas de testes de ferrament...
VISUAL-ID – Unique Visual Identities in Graphics, Images and Faces
The Visual-ID project emerge in the context of the partnership between the Imprensa Nacional-Casa da...
FACING2 – Face Image Understanding
The FACING-2 project aims to study and develop methods that allow exploring facial biometrics of hum...
TruIM – Trust Image Understanding
TruIm Project aims at developing technologies to authenticate objects in certified images, encoded u...
Publications
Biometric System for Mobile Validation of ID And Travel Documents
Current trends in security of ID and travel documents require portable and efficient validation applications that rely on biometric recognition. Such tools can allow any authority and citizen to validate documents and authenticate citizens with no need of expensive and sometimes unavailable proprietary devices. In this work, we present a novel, compact and efficient approach of validating ID and travel documents for offline mobile applications. The approach employs the in-house biometric template that is extracted from the original portrait photo (either full frontal or token frontal), and then stored on the ID document with use of a machine readable code (MRC). The ID document can then be validated with a developed application on a mobile device with digital camera. The similarity score is estimated with use of an artificial neural network (ANN). Results show that we achieve validation accuracy up to 99.5% with corresponding false match rate = 0.0047 and false non-match rate = 0.00034. (CITATION: I. Medvedev, N. Gonçalves and L. Cruz, "Biometric System for Mobile Validation of ID And Travel Documents," 2020 International Conference of the Biometrics Special Interest Group (BIOSIG), 2020, pp. 1-5.)
- Date: 01/10/2020
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- Featured In: 2020 International Conference of the Biometrics Special Interest Group (BIOSIG), Darmstadt, Germany, pp. 1-5
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- Publication Type: Conference Papers
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- Author(s): Iurii Medvedev, Nuno Gonçalves, Leandro Cruz
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Towards Facial Biometrics for ID Document Validation in Mobile Devices
Various modern security systems follow atendency to simplify the usage of the existing biometric recognition solutions and embed them into ubiquitous portable devices. In this work, we continue the investigation and development of our method for securing identification documents. The original facial biometric template, which is extracted from the trusted frontal face image, is stored on the identification document in a secured personalized machine-readable code. Such document is protected from face photo manipulation and may be validated with an offline mobile application. We apply automatic methods of compressing the developed face descriptors to make the biometric validation system more suitable for mobile applications. As an additional contribution, we introduce several print-capture datasets that may be used for training and evaluating similar systems for mobile identification and travel documents validation.
- Date: 01/07/2021
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- Featured In: Applied Sciences
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- Publication Type: Journal Articles
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- Author(s): Iurii Medvedev, Farhad Shadmand, Leandro Cruz, Nuno Gonçalves
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- DOI: 10.3390/app11136134
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QualFace: Adapting Deep Learning Face Recognition for ID and Travel Doc with Quality Assessment
Modern face recognition biometrics widely rely on deep neural networks that are usually trained on large collections of wild face images of celebrities. This choice of the data is related with its public availability in a situation when existing ID document compliant face image datasets (usually stored by national institutions) are hardly accessible due to continuously increasing privacy restrictions. However this may lead to a leak in performance in systems developed specifically for ID document compliant images. In this work we proposed a novel face recognition approach for mitigating that problem. To adapt deep face recognition network for document security purposes, we propose to regularise the training process with specific sample mining strategy which penalises the samples by their estimated quality, where the quality metric is proposed by our work and is related to the specific case of face images for ID documents. We perform extensive experiments and demonstrate the efficiency of proposed approach for ID document compliant face images.
- Date: 01/08/2021
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- Featured In: BIOSIG 2021
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- Publication Type: Conference Papers
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- Author(s): João Tremoço, Iurii Medvedev, Nuno Gonçalves
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CodeFace: a deep learning printer-proof steganography for Face Portraits.
Identity Documents (IDs) containing a facial portrait constitute a prominent form of personal identification. Photograph substitution in official documents (a genuine photo replaced by a non- genuine photo) or originally fraudulent documents with an arbitrary photograph are well known attacks, but unfortunately still efficient ways of misleading the national authorities in in-person identification processes. Therefore, in order to confirm that the identity document holds a validated photo, a novel face image steganography technique to encode secret messages in facial portraits and then decode these hidden messages from physically printed facial photos of Identity Documents (IDs) and Machine-Readable Travel Documents (MRTDs), is addressed in this paper. The encoded face image looks like the original image to a naked eye. Our architecture is called CodeFace. CodeFace comprises a deep neural network that learns an encoding and decoding algorithm to robustly include several types of image perturbations caused by image compression, digital transfer, printer devices, environmental lighting and digital cameras. The appearance of the encoded facial photo is preserved by minimizing the distance of the facial features between the encoded and original facial image and also through a new network architecture to improve the data restoration for small images. Extensive experiments were performed with real printed documents and smartphone cameras. The results obtained demonstrate high robustness in the decoding of hidden messages in physical polycarbonate and PVC cards, as well as the stability of the method for encoding messages up to a size of 120 bits.
- Date: 29/10/2021
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- Featured In: IEEE Access
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- Publication Type: Journal Articles
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- Author(s): F. Shadmand, I. Medvedev and N. Gonçalves
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- DOI: 10.1109/ACCESS.2021.3132581
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MorDeephy: Face Morphing Detection Via Fused Classification (preprint)
Face morphing attack detection (MAD) is one of the most challenging tasks in the field of face recognition nowadays. In this work, we introduce a novel deep learning strategy for a single image face morphing detection, which implies the discrimination of morphed face images along with a so- phisticated face recognition task in a complex classification scheme. It is directed onto learning the deep facial features, which carry information about the authenticity of these fea- tures. Our work also introduces several additional contributions: the public and easy-to-use face morphing detection benchmark and the results of our wild datasets filtering strategy. Our method, which we call MorDeephy, achieved the state of the art performance and demonstrated a promi- nent ability for generalising the task of morphing detection to unseen scenarios.
- Date: 05/08/2022
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- Featured In: arXiv
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- Publication Type: Journal Articles
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- Author(s): Iurii Medvedev, Farhad Shadmand, Nuno Gonçalves
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- DOI: 10.48550/arXiv.2208.03110
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Towards understanding the character of quality sampling in deep learning face recognition
Face recognition has become one of the most important modalities of biometrics in recent years. It widely utilises deep learning computer vision tools and adopts large collections of unconstrained face images of celebrities for training. Such choice of the data is related to its public availability when existing document compliant face image collections are hardly accessible due to security and privacy issues. Such inconsistency between the training data and deploy scenario may lead to a leak in performance in biometric systems, which are developed speci cally for dealing with ID document compliant images. To mitigate this problem, we propose to regularise the training of the deep face recognition network with a speci c sample mining strategy, which penalises the samples by their estimated quality. In addition to several considered quality metrics in recent work, we also expand our deep learning strategy to other sophisticated quality estimation methods and perform experiments to better understand the nature of quality sampling. Namely, we seek for the penalising manner (sampling character) that better satis es the purpose of adapting deep learning face recognition for images of ID and travel documents. Extensive experiments demonstrate the ef ciency of the approach for ID document compliant face images.
- Date: 05/08/2022
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- Featured In: IETBiometrics
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- Publication Type: Journal Articles
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- Author(s): Iurii Medvedev, João Tremoço, Beatriz Mano, Luís Espírito-Santo and Nuno Gonçalves
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- DOI: 10.1049/bme2.12095
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Improving Performance of Facial Biometrics With Quality-Driven Dataset Filtering
Advancements in deep learning techniques and availability of large scale face datasets led to signi cant perfor- mance gains in face recognition in recent years. Modern face recognition algorithms are trained on large- scale in-the-wild face datasets. At the same time, many facial biometric applications rely on controlled image acquisition and enrollment procedures (for instance, document security applications). That is why such face recognition approaches can demonstrate the de ciency of the performance in the target scenario (ICAO-compliant images). However, modern approaches for face image quality estimation may help to mitigate that problem. In this work, we introduce a strategy for ltering training datasets by quality metrics and demonstrate that it can lead to performance improvements in biometric applications that rely on face image modality. We lter the main academic datasets using the proposed ltering strategy and present performance metrics.
- Date: 05/01/2023
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- Featured In: Workshop on Interdisciplinary Applications of Biometrics and Identity Science (INTERID’2023), Hawaii
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- Publication Type: Conference Papers
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- Author(s): Iurii Medvedev and Nuno Gonçalves
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- DOI: 10.1109/FG57933.2023.10042579
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MorDeephy: Face Morphing Detection via Fused Classification
Face morphing attack detection (MAD) is one of the most challenging tasks in the field of face recognition nowadays. In this work, we introduce a novel deep learning strategy for a single image face morphing detection, which implies the discrimination of morphed face images along with a sophisticated face recognition task in a complex classification scheme. It is directed onto learning the deep facial features, which carry information about the authenticity of these features. Our work also introduces several additional contributions: the public and easy-to-use face morphing detection benchmark and the results of our wild datasets filtering strategy. Our method, which we call MorDeephy, achieved the state of the art performance and demonstrated a prominent ability for generalizing the task of morphing detection to unseen scenarios.
- Date: 22/02/2023
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- Featured In: 12th International Conference on Pattern Recognition Application and Methods (ICPRAM), Lisbon, Portugal.
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- Publication Type: Conference Papers
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- Author(s): Iurii Medvedev, Farhad Shadmand and Nuno Gonçalves
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Impact of Image Context for Single Deep Learning Face Morphing Attack Detection
The increase in security concerns due to techno- logical advancements has led to the popularity of biometric approaches that utilize physiological or behavioral characteris- tics for enhanced recognition. Face recognition systems (FRSs) have become prevalent, but they are still vulnerable to image manipulation techniques such as face morphing attacks. This study investigates the impact of the alignment settings of input images on deep learning face morphing detection performance. We analyze the interconnections between the face contour and image context and suggest optimal alignment conditions for face morphing detection.
- Date: 22/09/2023
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- Featured In: 2023 International Conference of the Biometrics Special Interest Group (BIOSIG)
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- Publication Type: Conference Papers
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- Author(s): Joana Alves Pimenta, Iurii Medvedev and Nuno Gonçalves
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- DOI: 10.1109/BIOSIG58226.2023.10345999
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Fused Classification for Differential Face Morphing Detection
Face morphing, a sophisticated presentation attack tech- nique, poses signi cant security risks to face recognition systems. Traditional methods struggle to detect morph- ing attacks, which involve blending multiple face images to create a synthetic image that can match different individ- uals. In this paper, we focus on the differential detection of face morphing and propose an extended approach based on fused classi cation method for no-reference scenario. We introduce a public face morphing detection benchmark for the differential scenario and utilize a speci c data min- ing technique to enhance the performance of our approach. Experimental results demonstrate the effectiveness of our method in detecting morphing attacks.
- Date: 04/01/2024
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- Featured In: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2024)
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- Publication Type: Conference Papers
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- Author(s): Iurii Medvedev, Joana Alves Pimenta and Nuno Gonçalves
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- DOI: 10.48550/arXiv.2309.00665
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