In disciplines such as digital pathology, the management of vast amounts of data, primarily ultra-high-resolution images, remains a significant barrier to the widespread adoption and seamless sharing of knowledge. Current research efforts are heavily focused on image encoding, often overlooking equally critical aspects such as indexing and efficient content transmission. Traditional compression methods, such as JPEG2000, prioritize reconstruction quality but do not inherently support direct retrieval or progressive transmission, both of which are essential for applications like telemedicine and large-scale digital pathology archives. To bridge this gap, we introduce a novel framework that integrates fractal compression, deep learning-based retrieval, and adaptive transmission, optimizing not only storage efficiency but also accessibility and scalability in histopathological imaging. The Histopathological image Organization and Processing Environment (HOPE) framework here proposed exploits Partitioned Iterated Function Systems for image compression, achieving high compression ratios while preserving essential structural details. To mitigate the inherent artifacts of fractal compression, a U-Net autoencoder is integrated, refining decompressed images and enhancing visual quality. Additionally, a residual encoding mechanism is employed, allowing for lossless reconstruction when necessary. Unlike conventional methods, this framework enables direct retrieval from the compressed domain by extracting discriminative features from the fractal encoding coefficients. Another key innovation is its progressive transmission capability, which allows an initial low-bitrate preview to be sent, followed by incremental quality refinements based on diagnostic needs. This significantly reduces network load and enables real-time access to high-resolution histopathological images on resource-limited devices. Experimental results demonstrate that the proposed framework achieves compression performance comparable to JPEG2000, while simultaneously enabling efficient indexing, high-accuracy retrieval, and scalable transmission. • Histopathological image Organization and Processing Environment (HOPE) is a modular architecture that leverages fractal theory to efficiently compress, index, retrieve, and transmit histopathological images. • The compression pipeline is designed to fully exploit the properties of Partitioned Iterated Function Systems (PIFS), with a U-Net autoencoder for reconstructing lost information. • HOPE introduces an advanced indexing system that directly operates on the fractal encoding coefficients, eliminating the need to decode images for similarity-based retrieval. • HOPE supports progressive transmission of data, allowing for an initial transmission at low bitrates, followed by progressive quality refinements.

Biometric authentication is now widely adopted across both consumer and enterprise applications, offering an intuitive and seamless user experience. However, research in the field has historically focused more on improving recognition accuracy than on addressing fundamental security and privacy concerns. As biometric technologies become increasingly pervasive, their exposure to a broad range of threats, including template leakage, identity theft, and cross-matching, raises critical challenges. Existing protection schemes such as cancelable biometrics, biometric cryptosystems, and privacy-preserving protocols suffer from significant limitations: they are often vulnerable to inversion, prone to residual linkability across re-issuances, or computationally unsuited for edge devices. To address these issues, this work introduces SHADOW (Secure Hidden Authenticating Digital Objects in the Wild), a framework for secure and privacy-preserving biometric authentication based on on-device generation of non-invertible and revocable representations. SHADOW achieves template-protection properties including unlinkability and renewability. It leverages analytically defined oscillatory functions to construct a structured authenticating object that binds a hidden certificate to the protected template and supports client-aware verification via nonce-driven masking. Trusted hardware can be used as optional hardening for endpoint secret protection; it is not required for SHADOW's template-protection guarantees, while client integrity assumes the confidentiality of the device-held secret. The proposed binding strategy achieves formal irreversibility and unlinkability by design, while supporting renewability and minimal computational overhead. Experiments conducted on public face, iris and voice benchmarks demonstrate that SHADOW achieves comparable recognition performance while ensuring robustness against major attack vectors, including inversion, brute-force, and record multiplicity. The framework is compatible with modern embedding-based biometrics and exhibits properties that can facilitate deployment in decentralized and edge computing environments.

The need for automated systems to aid law enforcement during densely packed events arises from the inherent danger of large crowds, evidenced by historical instances of stampedes and crushes. Existing methods vary from basic crowd statistics extraction to detailed anomaly detection in behavior classification, but often focus on single, pre-segmented scenes. Our work addresses classifying crowd behaviors in environments where multiple behaviors coexist within a single scene, defined as a multi-class crowd motion characterization challenge. We use a microscopic approach for scenes captured by drones at varying altitudes, without prior manipulation. This approach combines graph-based representations of individuals and flow images, facilitating classification of diverse crowd behaviors in unsegmented scenes. Tested on a public dataset, our method shows promising results in analyzing complex crowd dynamics.

Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens highly depend on the phenotype and topological distribution of constituting histological entities. Thus, adequate tissue representations for encoding histological entities is imperative for computer aided cancer patient care. To this end, several approaches have leveraged cell-graphs, capturing the cell-microenvironment, to depict the tissue. These allow for utilizing graph theory and machine learning to map the tissue representation to tissue functionality, and quantify their relationship. Though cellular information is crucial, it is incomplete alone to comprehensively characterize complex tissue structure. We herein treat the tissue as a hierarchical composition of multiple types of histological entities from fine to coarse level, capturing multivariate tissue information at multiple levels. We propose a novel multi-level hierarchical entity-graph representation of tissue specimens to model the hierarchical compositions that encode histological entities as well as their intra- and inter-entity level interactions. Subsequently, a hierarchical graph neural network is proposed to operate on the hierarchical entity-graph and map the tissue structure to tissue functionality. Specifically, for input histology images, we utilize well-defined cells and tissue regions to build HierArchical Cell-to-Tissue (HACT) graph representations, and devise HACT-Net, a message passing graph neural network, to classify the HACT representations. As part of this work, we introduce the BReAst Carcinoma Subtyping (BRACS) dataset, a large cohort of Haematoxylin & Eosin stained breast tumor regions-of-interest, to evaluate and benchmark our proposed methodology against pathologists and state-of-the-art computer-aided diagnostic approaches. Through comparative assessment and ablation studies, our proposed method is demonstrated to yield superior classification results compared to alternative methods as well as individual pathologists. The code, data, and models can be accessed at https://github.com/histocartography/hact-net.

The analysis of the shape and geometry of the human hand has long represented an attractive field of research to address the needs of digital image forensics. Over recent years, it has also turned out to be effective in biometrics, where several innovative research lines are pursued. Given the widespread diffusion of mobile and portable devices, the possibility of checking the owner identity and controlling the access to the device by the hand image looks particularly attractive and less intrusive than other biometric traits. This encourages new research to tacklethe present limitations. The proposed work implements the complete architecture of a mobile hand recognition system, which uses the camera of the mobile device for the acquisition of the hand in visible light spectrum. The segmentation of the hand starts from the detection of the convexities and concavities defined by the fingers, and allows extracting 57 different features from the hand shape. The main contributions of the paper develop along two directions. First, dimensionality reduction methods are investigated, in order to identify subsets of features including only the most discriminating and robust ones. Second, different matching strategies are compared. The proposed method is tested over a dataset of hands from 100 subjects. The best obtained Equal Error Rate is 0.52%. Results demonstrate that discarding features that are more prone to distortions allows lighter processing, but also produces better performance than using the full set of features. This confirms the feasibility of such an approach on mobile devices, and further suggests to adopt it even in more traditional settings.

New technological advances in automated microscopy have given rise to large volumes of data, which have made human-based analysis infeasible, heightening the need for automatic systems for high-throughput microscopy applications. In particular, in the field of fluorescence microscopy, automatic tools for image analysis are making an essential contribution in order to increase the statistical power of the cell analysis process. The development of these automatic systems is a difficult task due to both the diversification of the staining patterns and the local variability of the images. In this paper, we present an unsupervised approach for automatic cell segmentation and counting, namely CSC, in high-throughput microscopy images. The segmentation is performed by dividing the whole image into square patches that undergo a gray level clustering followed by an adaptive thresholding. Subsequently, the cell labeling is obtained by detecting the centers of the cells, using both distance transform and curvature analysis, and by applying a region growing process. The advantages of CSC are manifold. The foreground detection process works on gray levels rather than on individual pixels, so it proves to be very efficient. Moreover, the combination of distance transform and curvature analysis makes the counting process very robust to clustered cells. A further strength of the CSC method is the limited number of parameters that must be tuned. Indeed, two different versions of the method have been considered, CSC-7 and CSC-3, depending on the number of parameters to be tuned. The CSC method has been tested on several publicly available image datasets of real and synthetic images. Results in terms of standard metrics and spatially aware measures show that CSC outperforms the current state-of-the-art techniques.

In this work, we propose a deep learning approach for the detection of the activities of daily living (ADL) in a home environment starting from the skeleton data of an RGB-D camera. In this context, the combination of ad hoc features extraction/selection algorithms with supervised classification approaches has reached an excellent classification performance in the literature. Since the recurrent neural networks (RNNs) can learn temporal dependencies from instances with a periodic pattern, we propose two deep learning architectures based on Long Short-Term Memory (LSTM) networks. The first (MT-LSTM) combines three LSTMs deployed to learn different time-scale dependencies from pre-processed skeleton data. The second (CNN-LSTM) exploits the use of a Convolutional Neural Network (CNN) to automatically extract features by the correlation of the limbs in a skeleton 3D-grid representation. These models are tested on the CAD-60 dataset. Results show that the CNN-LSTM model outperforms the state-of-the-art performance with 95.4% of precision and 94.4% of recall.

This paper presents and discusses an Enterprise Rights Management System (ERM) tailored for remote maintenance facilities which features secure on-site documentation storage and availability, strong data provider control over access policy and their enforcement, minimal documentation disclosure and strong and ergonomic operator authentication by means of biometric measurements. The design approach pursues the technological neutrality with respect to the documentation format and operator side equipments.

The ability of a biometric system to reliably recognize registered individuals significantly depends on the kind and amount of variation that the exploited biometric trait may undergo throughout acquisitions. Those variations may be due both to acquisition devices, or to different environment settings, or to modification of the trait appearance. One of the strategies to address changes in biometric features is to store more templates for the same person, in order to increase the chances to identify her. The problem arises to choose the templates to store in a way which actually achieves better performance, while avoiding flooding the system with an excessively huge gallery. This work proposes an approach for the selection of the best templates for face recognition, which is based on a notion of gallery entropy, and can be also used for large datasets. Though relying on a clustering process, its main achievement is to automatically derive the best number of clusters/prototypes per subject without requiring to fixing it in advance. Comparative tests with existing approaches show that it is a very promising solution.

In this paper an iris detection scheme for noisy images acquired by means of mobile devices is presented. Iris segmentation is accomplished by exploiting the use of the watershed transform with the purpose of identifying the iris boundary as much precisely as possible. After a pre-processing step aimed at color/illumination correction, the watershed transform is computed and suitably binarized. Circle fitting is then accomplished to identify the limbus boundary by using curvature approximation and a cost function for circle scoring. The watershed transform is furthermore employed to distinguish, in the zone delimited by the best fitting circle, the regions actually belonging to the iris from those belonging to eyelids and sclera. Finally, pupil detection is accomplished by means of circle fitting and by using a voting function based on homogeneity and separability criteria. The suggested iris detection scheme has a positive impact on an the accuracy in computing the iris code, which has in turn a positive impact on the performance of iris recognition.

Advanced surveillance applications often require the ability to re-identify an individual. In the typical context of a sensor network, this means to recognize a subject acquired at one location among a feasible set of candidates acquired at another locations and/or over time. Usually this does not necessarily imply to know the identity, and actually it might remain unknown. This task is especially challenging in applications targeted at crowded environments. Face and gait are contactless biometrics which are particularly suited to be used for re-identification, but even “soft” biometrics have also been considered to this aim. This paper presents a comparison of approaches to re-identification, with some characteristic examples in literature, while a complete overview of the main researches is out of our scope. The goal is to provide an objective estimate of both the state-of-the-art and the potential of such techniques to further improve them and to extend the applicability of re-identification systems.

Multi-biometric systems are a powerful solution to deal with limitations of single classifiers, therefore improving the final recognition accuracy. The sub-systems composing the final architecture often return supplementary indices of input quality and/or of response reliability, which further qualify each recognition score. These indices can enter different information fusion policies. First, they can be used as weights for the fusion of the corresponding scores, in such a way that less trustworthy responses have a lower influence. Alternatively, they can be used to drive the selection of a subset of systems actually enabled for each fusion operation. The present work discusses their appropriate combination with respective scores, to obtain single values which are easier to handle and compare. It is worth underlining the different nature of quality and reliability measures. The quality estimation of input samples requires a complex analysis of environmental conditions, including capture sensors, besides computations over acquired data. Reliability of a system estimates its ability to return a correct response. As an alternative to combination, some solutions rather estimate the joint distributions of conditional probabilities of the scores from the single subsystems. These solutions require training through a huge number of samples. Furthermore, they assume stable score distributions. Our unified representation of the recognition score and of the corresponding quality/reliability value into a single complex number provides simplification and speed up of fusion of multi-classifier results. It also allows to devise procedures to readily compare the performance of different modules in a multi-biometric system, given that there is no natural ordering of these pairs of values of different nature. Moreover our method achieves performance comparable to top performing schemes, yet does not require a prior estimation of (joint) score distributions. As a matter of fact, though representing an upper bound to the obtainable performance, Likelihood ratio has the limit to require an accurate estimation of score distributions, while our approach relies on the reliability of each single response. This feature is very interesting when the set of relevant subjects may present significant variations over time.

Face recognition has made significant advances in the last decade, but robust commercial applications are still lacking. Current authentication/identification applications are limited to controlled settings, e.g., limited pose and illumination changes, with the user usually aware of being screened and collaborating in the process. Among others, pose and illumination changes are limited. To address challenges from looser restrictions, this paper proposes a novel framework for real-world face recognition in uncontrolled settings named Face Analysis for Commercial Entities (FACE). Its robustness comes from normalization (“correction”) strategies to address pose and illumination variations. In addition, two separate image quality indices quantitatively assess pose and illumination changes for each biometric query, before submitting it to the classifier. Samples with poor quality are possibly discarded or undergo a manual classification or, when possible, trigger a new capture. After such filter, template similarity for matching purposes is measured using a localized version of the image correlation index. Finally, FACE adopts reliability indices, which estimate the “acceptability” of the final identification decision made by the classifier. Experimental results show that the accuracy of FACE (in terms of recognition rate) compares favorably, and in some cases by significant margins, against popular face recognition methods. In particular, FACE is compared against SVM, incremental SVM, principal component analysis, incremental LDA, ICA, and hierarchical multiscale local binary pattern. Testing exploits data from different data sets: CelebrityDB, Labeled Faces in the Wild, SCface, and FERET. The face images used present variations in pose, expression, illumination, image quality, and resolution. Our experiments show the benefits of using image quality and reliability indices to enhance overall accuracy, on one side, and to provide for individualized processing of biometric probes for better decision-making purposes, on the other side. Both kinds of indices, owing to the way they are defined, can be easily integrated within different frameworks and off-the-shelf biometric applications for the following: 1) data fusion; 2) online identity management; and 3) interoperability. The results obtained by FACE witness a significant increase in accuracy when compared with the results produced by the other algorithms considered.

VIVIE is a system for video sequence indexing. Video frames are annotated according to the identities of appearing subjects. Different interacting modules perform different processing steps, and each can be possibly substituted with a different one performing the same task using a different method. Classification and clustering are the most challenging activities. Differently from most existing similar systems, VIVIE accounts for the concomitant appearance of two identities in the same clip, and exploits such information for identity mapping. VIVIE was tested on 7 video clips and on a subset of the SCFace database to assess its performances.

Research on biometrics has noticeably increased. However, no single bodily or behavioral feature is able to satisfy acceptability, speed, and reliability constraints of authentication in real applications. The present trend is therefore toward multimodal systems. In this paper, we deal with some core issues related to the design of these systems and propose a novel modular framework, namely, novel approaches for biometric systems (NABS) that we have implemented to address them. NABS proposal encompasses two possible architectures based on the comparative speeds of the involved biometries. It also provides a novel solution for the data normalization problem, with the new quasi-linear sigmoid (QLS) normalization function. This function can overcome a number of common limitations, according to the presented experimental comparisons. A further contribution is the system response reliability (SRR) index to measure response confidence. Its theoretical definition allows to take into account the gallery composition at hand in assigning a system reliability measure on a single-response basis. The unified experimental setting aims at evaluating such aspects both separately and together, using face, ear, and fingerprint as test biometries. The results provide a positive feedback for the overall theoretical framework developed herein. Since NABS is designed to allow both a flexible choice of the adopted architecture, and a variable compositions and/or substitution of its optional modules, i.e., QLS and SRR, it can support different operational settings.

Though face recognition gained significant attention and credibility in the last decade, quite few commercial applications were able to benefit from this. In this paper we propose FACE (Face analysis for Commercial Entities), a robust framework to address face analysis, aiming at supporting the activities of various Commercial Entities. In particular, we present two case studies, with the related experimental results which sustain the presented approach.

Advances in processing procedures make the iris a realistic candidate to the role of biometry of the future. Precise detection and segmentation for such biometry are a crucial ongoing research area. We propose an iris segmentation technique and show that it is more reliable than existent ones.

Face recognition is widely considered as one of the most promising biometric techniques, allowing high recognition rates without being too intrusive. Many approaches have been presented to solve this special pattern recognition problem, also addressing the challenging cases of face changes, mainly occurring in expression, illumination, or pose. On the other hand, less work can be found in literature that deals with partial occlusions (i.e., sunglasses and scarves). This paper presents face recognition against occlusions and expression variations (FARO) as a new method based on partitioned iterated function systems (PIFSs), which is quite robust with respect to expression changes and partial occlusions. In general, algorithms based on PIFSs compute a map of self-similarities inside the whole input image, searching for correspondences among small square regions. However, traditional algorithms of this kind suffer from local distortions such as occlusions. To overcome such limitation, information extracted by PIFS is made local by working independently on each face component (eyes, nose, and mouth). Distortions introduced by likely occlusions or expression changes are further reduced by means of anad hocdistance measure. In order to experimentally confirm the robustness of the proposed method to both lighting and expression variations, as well as to occlusions, FARO has been tested using AR-faces database, one of the main benchmarks for the scientific community in this context. A further validation of FARO performances is provided by the experimental results produced on Face Recognition Grand Challenge database.

The structure of a fractal based image indexing system is described. FINE implies image space linearization, a custom clustering strategy, Peano-serialized spatial addressing, ad-hoc heuristics for improving search and specially defined distance functions. The resulting system is invariant, or at least robust, to a large class of typical variations that appear in natural images including rotations, scaling, and changes in color or illumination. The performance of FINE is illustrated, discussed and compared with other present alternatives using standard and custom-based image databases.

During the last few years, many algorithms have been proposed in particular for face recognition using classical 2-D images. However, it is necessary to deal with occlusions when the subject is wearing sunglasses, scarves and such. In the same way, ear recognition is arising as a new promising biometric for people recognition, even if the related literature appears to be somewhat underdeveloped. In this paper, several hybrid face/ear recognition systems are investigated. The system is based on IFS (Iterated Function Systems) theory that are applied on both face and ear resulting in a bimodal architecture. One advantage is that the information used for the indexing and recognition task of face/ear can be made local, and this makes the method more robust to possible occlusions. The distribution of similarities in the input images is exploited as a signature for the identity of the subject. The amount of information provided by each component of the face and the ear image has been assessed, first independently and then jointly. At last, results underline that the system significantly outperforms the existing approaches in the state of the art.

Fractals can be an effective approach for several applications other than image coding and transmission: database indexing, texture mapping, and even pattern recognition problems such as writer authentication. However, fractal-based algorithms are strongly asymmetric because, in spite of the linearity of the decoding phase, the coding process is much more time consuming. Many different solutions have been proposed for this problem, but there is not yet a standard for fractal coding. This paper proposes a method to reduce the complexity of the image coding phase by classifying the blocks according to an approximation error measure. It is formally shown that postponing range\slash domain comparisons with respect to a preset block, it is possible to reduce drastically the amount of operations needed to encode each range. The proposed method has been compared with three other fractal coding methods, showing under which circumstances it performs better in terms of both bit rate and/or computing time.

Nowadays face recognition is gaining great attention from the researcher respect to other biometrics, because it represents a good compromise between reliability and people acceptance. While this growth largely is driven by growing application demands, such as identification for law enforcement and authentication, for banking and security system access. The recognition task is difficult because of image variation in terms of position, size, expression, and pose. In this paper a new IFS based recognition method is presented. It exploits the IFS theory, largely studied in still image compression and indexing, but not enough for the face recognition task.