Morphing Attack Detection (MAD) systems often suffer from performance degradation when deployed in operational environments, such as airports, that differ from the training domain.We propose an adaptive differential MAD framework that continuously refines a pre-trained detector using live bona fide samples acquired at the gate.The system is memoryless, so no samples are stored in memory to mitigate privacy concerns about the collection of personal data.To prevent the loss of discriminative power caused by bona fideonly adaptation, the method generates synthetic morph samples on-the-fly by combining the current operational subject with identities from an external public or synthetic face dataset.The adaptation process further relies on a quality-aware bona fide selection strategy and a controlled balancing mechanism for synthetic morph generation.Experimental results show that the proposed method improves target-domain specialization while maintaining robustness against morph attacks.

Face morphing attacks are widely recognized as one of the most challenging threats to face recognition systems used in electronic identity documents. These attacks exploit a critical vulnerability in passport enrollment procedures adopted by many countries, where the facial image is often acquired without a supervised live capture process. In this paper, we propose a novel face morphing technique based on Arc2Face, an identity-conditioned face foundation model capable of synthesizing photorealistic facial images from compact identity representations. We demonstrate the effectiveness of the proposed approach by comparing the morphing attack potential metric on two large-scale sequestered face morphing attack detection datasets against several state-of-the-art morphing methods, as well as on two novel morphed face datasets derived from FEI and ONOT. Experimental results show that the proposed deep learning-based approach achieves a morphing attack potential comparable to that of landmark-based techniques, which have traditionally been regarded as the most challenging. These findings confirm the ability of the proposed method to effectively preserve and manage identity information during the morph generation process.

• A fully automated pipeline for barley root extraction and characterization. • DeepRoot-3H : multi-head network for segmenting roots, tips, and sources. • Post-processing stage handling overlaps and dense root clusters. • Graph-based path analysis for RSML generation and trait extraction • High accuracy and robustness on challenging barley root image datasets Understanding plant root architecture under diverse environmental conditions is crucial for improving crop resilience and ensuring global food security. We present a fully automated method for segmenting barley root systems from high-resolution images and detecting keypoints such as tips and sources with high precision. At the core of our approach is DeepRoot-3H , a novel multi-head deep network built upon the DeepLabv3+ backbone, designed to jointly handle root segmentation and keypoint detection within a unified architecture. This integrated design enhances both the consistency and robustness of the outputs. A dedicated post-processing stage further refines keypoint localization, effectively handling challenges such as dense root clusters and variability in image quality. The resulting predictions are then structured into a graph representation, on which a path-walking algorithm identifies biologically meaningful connections between tips and sources. This enables the generation of RSML files and the extraction of critical morphological traits. To evaluate the system, we employ IoU and Dice scores for segmentation quality, alongside Euclidean and weighted distance metrics for tip and source detection. We also assess the biological consistency of the extracted traits—such as total root length, tortuosity, covered area, and outer angles—through correlation and discrepancy measures. Experimental results on a challenging benchmark dataset demonstrate significant improvements over existing techniques, confirming the effectiveness and reliability of our method for high-fidelity root system analysis.

Facial recognition is a key biometric technology, especially for using electronic documents in real-world applications. The accuracy of this recognition technology strictly depends on the image quality, i.e. the face appearance in the image included in the document. Then, adherence to ISO/ICAO standards, which contain guidelines to standardize the image quality in official documents, is of paramount importance. However, ensuring compliance is challenging due to high subject variability. Furthermore, controls are often executed manually, making them subjective and time-consuming. Therefore, in this work, we introduce BioGaze, an automated framework for ISO/ICAO compliance verification that combines classical computer vision and deep learning algorithms to perform the checks contained in the latest standard version. The framework is tested on a synthetic dataset, achieving state-of-the-art performance across multiple ISO/ICAO requirements, surpassing public algorithms and commercial SDKs. BioGaze is publicly available to advance automated compliance verification and support standardization efforts1.1https://github.com/MI-BioLab/BioGaze

Ensuring compliance of face images with ISO/ICAO quality standards is essential for boosting the document enrollment process. Indeed, traditional manual checks are slow, subjective, and difficult to scale. Therefore, we propose a system that aims to fully automate compliance verification by directly analyzing the official requirements without relying on predefined hand-crafted features or manual thresholds. Our method combines a Large Language Model, a novel prompt learning procedure, and a contrastive learning framework to evaluate the adherence of a face image to quality requirements. Tested on a recent dataset, our proposed system achieves high accuracy, surpassing existing academic and commercial solutions. By streamlining the implementation and updates to the compliance rules, our approach represents a significant step toward simple, scalable, and regulation-driven image verification. Code and models are publicly available1.

In recent years, computer vision and deep learning have become increasingly important in the livestock industry, offering innovative animal monitoring and farm management solutions. This paper focuses on the critical task of cattle segmentation, an essential application for weight estimation, body condition scoring, and behavior analysis. Despite advances in segmentation techniques, accurately identifying and isolating cattle in complex farm environments remains challenging due to varying lighting conditions and overlapping objects. This study evaluates state-of-the-art segmentation models based on convolutional neural networks and transformers, which leverage self-attention mechanisms to capture long-range image dependencies. By testing these models across multiple publicly available datasets, we assess their performance and generalization capabilities, providing insights into the most effective methods for accurate cattle segmentation in real-world farm conditions. We also explore ensemble techniques, selecting pairs of segmenters with maximum diversity. The results are promising, as an ensemble of only two models improves performance over all stand-alone methods. The findings contribute to improving computer vision-based solutions for livestock management, enhancing their accuracy and reliability in practical applications.

Nowadays, state-of-the-art AI-based generative models represent a viable solution to overcome privacy issues and biases in the collection of datasets containing personal information, such as faces. Following this intuition, in this paper we introduce ONOT11One, No one and One hundred Thousand (L. Pirandello, 1926), a synthetic dataset specifically focused on the generation of high-quality faces in adherence to the requirements of the ISO/IEC 39794–5 standards that, following the guidelines of the International Civil Aviation Organization (ICAO), defines the interchange formats of face images in electronic Machine-Readable Travel Documents (eMRTD). The strictly controlled and varied mugshot images included in ONOT are useful in research fields related to the analysis of face images in eMRTD, such as Morphing Attack Detection and Face Quality Assessment. The dataset is publicly released22https://miatbiolab.csr.unibo.it/icao-synthetic-dataset, in combination with the generation procedure details in order to improve the reproducibility and enable future extensions.

In response to the rising threat of the face morphing attack, this paper introduces and explores the potential of Video-based Morphing Attack Detection (V-MAD) systems in real-world operational scenarios. While current morphing attack detection methods primarily focus on a single or a pair of images, V-MAD is based on video sequences, exploiting the video streams acquired by face verification tools available, for instance, at airport gates. We show for the first time the advantages that the availability of multiple probe frames brings to the morphing attack detection task, especially in scenarios where the quality of probe images is varied. Experimental results on a real operational database demonstrate that video sequences represent valuable information for increasing the performance of morphing attack detection systems.

The morphing attack is widely acknowledged as an important security threat to face recognition systems in the context of electronic machine readable travel documents and several possible countermeasures have been recently proposed. Among the existing solutions, differential Morphing Attack Detection (MAD) algorithms, based on the comparison of the document image (possibly morphed) and a trusted live capture, proved to be quite effective and robust in detecting this kind of attack. However, deploying such solutions in a real-world operational scenario requires the capability of dealing with images of variable quality in terms of illumination, pose, focus, etc. This paper analyzes the impact of face image quality on MAD performance through an extensive image quality assessment, carried out on a large and realistic operational dataset using different state-of-the-art algorithms, thus providing useful insights for the development of more robust MAD systems.

Through the Face Morphing attack is possible to use the same legal document by two different people, destroying the unique biometric link between the document and its owner. In other words, a morphed face image has the potential to bypass face verification-based security controls, then representing a severe security threat. Unfortunately, the lack of public, extensive and varied training datasets severely hampers the development of effective and robust Morphing Attack Detection (MAD) models, key tools in contrasting the Face Morphing attack since able to automatically detect the presence of morphing images. Indeed, privacy regulations limit the possibility of acquiring, storing, and transferring MAD-related data that contain personal information, such as faces. Therefore, in this paper, we investigate the use of Federated Learning to train a MAD model on local training samples across multiple sites, eliminating the need for a single centralized training dataset, as common in Machine Learning, and then overcoming privacy limitations. Experimental results suggest that FL is a viable solution that will need to be considered in future research works in MAD.

Scenarios in which restrictions in data transfer and storage limit the possibility to compose a single dataset – also exploiting different data sources – to perform a batch-based training procedure, make the development of robust models particularly challenging. We hypothesize that the recent Continual Learning (CL) paradigm may represent an effective solution to enable incremental training, even through multiple sites. Indeed, a basic assumption of CL is that once a model has been trained, old data can no longer be used in successive training iterations and in principle can be deleted. Therefore, in this paper, we investigate the performance of different Continual Learning methods in this scenario, simulating a learning model that is updated every time a new chunk of data, even of variable size, is available. Experimental results reveal that a particular CL method, namely Learning without Forgetting (LwF), is one of the best-performing algorithms. Then, we investigate its usage and parametrization in Morphing Attack Detection and Object Classification tasks, specifically with respect to the amount of new training data that became available.

Recently, the Face Morphing Attack has emerged as a serious and concrete security threat, and then several Morphing Attack Detectors (MADs) have been proposed in the literature. Unfortunately, most MAD approaches, especially if based on single input images, are not yet mature for real-world deployment, mainly due to low accuracy and limited generalization capabilities with data distribution different from those used for training. While better models and techniques will certainly contribute to advancing the state-of-the-art, one of the main obstacles is on the data side: indeed, training data available to research groups are often limited in size and variety, and privacy issues restrict the possibility of data release and exchange. The proposed approach envisages the incremental training of MADs on a collection of datasets even owned by different research groups, adopting a learning strategy that involves model transfer as an alternative to data sharing. Specifically, in this paper, we propose and publicly release a framework to support the adoption of Continual Learning strategies, enabling an efficient incremental training for MADs on new data that progressively become available at different places or times. Different learning strategies are analyzed and compared in terms of the effectiveness and stability of the results achieved.

The absence of a previous collaborative manual enrolment represents a significant handicap towards designing a face verification system for face re-identification purposes. In this scenario, the system must learn the target identity incrementally, using data from the video stream during the operational authentication phase. So, manual labelling cannot be assumed apart from the first few frames. On the other hand, even the most advanced methods trained on large-scale and unconstrained datasets suffer performance degradation when no adaptation to specific contexts is performed. This work proposes an adaptive face verification system, for the continuous re-identification of target identity, within the framework of incremental unsupervised learning. Our Dynamic Ensemble of SVM is capable of incorporating non-labelled information to improve the performance of any model, even when its initial performance is modest. The proposal uses the self-training approach and is compared against other classification techniques within this same approach. Results show promising behaviour in terms of both knowledge acquisition and impostor robustness.

Morphing attacks have posed a severe threat to Face Recognition System (FRS). Despite the number of advancements reported in recent works, we note serious open issues such as independent benchmarking, generalizability challenges and considerations to age, gender, ethnicity that are inadequately addressed. Morphing Attack Detection (MAD) algorithms often are prone to generalization challenges as they are database dependent. The existing databases, mostly of semi-public nature, lack in diversity in terms of ethnicity, various morphing process and post-processing pipelines. Further, they do not reflect a realistic operational scenario for Automated Border Control (ABC) and do not provide a basis to test MAD on unseen data, in order to benchmark the robustness of algorithms. In this work, we present a new sequestered dataset for facilitating the advancements of MAD where the algorithms can be tested on unseen data in an effort to better generalize. The newly constructed dataset consists of facial images from 150 subjects from various ethnicities, age-groups and both genders. In order to challenge the existing MAD algorithms, the morphed images are with careful subject pre-selection created from the contributing images, and further post-processed to remove morphing artifacts. The images are also printed and scanned to remove all digital cues and to simulate a realistic challenge for MAD algorithms. Further, we present a new online evaluation platform to test algorithms on sequestered data. With the platform we can benchmark the morph detection performance and study the generalization ability. This work also presents a detailed analysis on various subsets of sequestered data and outlines open challenges for future directions in MAD research.

Semi-supervised learning techniques are gaining importance in the scenario of constantly growing data collections. CBIR systems must be able to autonomously analyze the patterns available, to fully exploit unlabeled data with the final objective of identifying an optimal representation space where data belonging to the same semantic class are close to each other. In this work we propose to adopt relevance feedback as a mean of collecting information about the semantic classes perceived by the user and to exploit this information for a long-term learning process where a more effective feature space can be obtained by a proper metric learning technique and class labels can be automatically assigned to unlabeled patterns. The process can iterate as new data become available thus providing a tool for successfully managing new incoming data. The experimental results will confirm the advantages of the proposed learning approach.

The morphing attack proved to be a serious threat for modern automated border control systems where face recognition is used to link the identity of a passenger to his/her e-document. In this paper, we show that by exploiting the live face image acquired at the gate, the morphed face image stored in the document can be reverted (or demorphed) enough to reveal the identity of the legitimate document owner, thus allowing the system to issue a warning. A number of practical experiments on two data sets proves the efficacy of our approach.

Once upon a time there was a criminal; he was reading his e-mail when a banner caught his attention: low cost flights for the destination of his dreams! He had already started to book the trip when suddenly realized that, being wanted by the police, he could not use his passport without being arrested. What to do? He could not miss that opportunity, so he called a good friend and they started to think for a possible solution. Do you want to know if they succeeded? Read the rest of the paper and find it out.

Sketch recognition for forensic applications is a very challenging task and several solutions have been recently proposed. Considering that real mug shot databases can be very large, one important aspect to consider in this scenario is also the efficiency of the search procedure. This work proposes the use of shape features for a preliminary selection of the candidate photos to be successively analyzed by more complex state-of-the-art techniques. The proposed features can be computed and matched in a very short time, and at the same time they are able to significantly reduce the search space, thus allowing to speed up the recognition process.

A new approach for video-based face recognition is presented in this paper. The proposed technique is based on the use of key points and related descriptors for image representation. In particular this work introduces a general approach to extend to the temporal dimension the analysis usually carried out on single images, with the aim of deriving a more stable and compact representation of video information. The algorithm, which can easily be coupled with different techniques for key point extraction and representation, has been designed taking into account two fundamental requirements in this application scenario: recognition accuracy and efficiency of the matching process. The extensive experiments carried out on public datasets using a specific implementation based on SURF demonstrate the validity of the proposed technique.

Face images to be included into machine readable travel documents have to fulfill quality requirements defined by international ISO standards. The concept of quality in this context extends the common idea of image quality: usually a bad quality image presents visual defects such as blurring or noise while, according to ISO/ICAO standard, other factors could make a given sample a poor quality image (e.g., presence of dark glasses or mouth open). The verification of face image conformance to ISO/ICAO standards is carried out mostly by humans today, through visual inspection, since a totally automatic evaluation is still not satisfactory. The objective of this work is to present the BioLab-ICAO framework, an evaluation benchmark which will be made available to the scientific community, designed to encourage the research on this topic; it consists of a large ground truth database, a well-defined testing protocol, and baseline algorithms for image compliance verification.

This work focuses on performance assessment of software applications designed to evaluate the compliance of a face image to the ISO/ICAO standards for machine readable travel documents. In this paper we describe the new large database (of compliant and non-compliant images) we gathered, the associated testing protocol and the preliminary results measured on some existing algorithms.