arXiv subjects

Emanuele Frontoni

Publications and source records attributed to Emanuele Frontoni.

31 records · Page 2Linked to original sources

arXiv2024

Embedding Non-Distortive Cancelable Face Template Generation

Biometric authentication systems are crucial for security, but developing them involves various complexities, including privacy, security, and achieving high accuracy without directly storing pure biometric data in storage. We introduce an innovative image distortion technique that makes facial images unrecognizable to the eye but still identifiable by any custom embedding neural network model. Using the proposed approach, we test the reliability of biometric recognition networks by determining the maximum image distortion that does not change the predicted identity. Through experiments on MNIST and LFW datasets, we assess its effectiveness and compare it based on the traditional comparison metrics.

cs.CV↗

arXiv2024

Cross-Database Liveness Detection: Insights from Comparative Biometric Analysis

In an era where biometric security serves as a keystone of modern identity verification systems, ensuring the authenticity of these biometric samples is paramount. Liveness detection, the capability to differentiate between genuine and spoofed biometric samples, stands at the forefront of this challenge. This research presents a comprehensive evaluation of liveness detection models, with a particular focus on their performance in cross-database scenarios, a test paradigm notorious for its complexity and real-world relevance. Our study commenced by meticulously assessing models on individual datasets, revealing the nuances in their performance metrics. Delving into metrics such as the Half Total Error Rate, False Acceptance Rate, and False Rejection Rate, we unearthed invaluable insights into the models' strengths and weaknesses. Crucially, our exploration of cross-database testing provided a unique perspective, highlighting the chasm between training on one dataset and deploying on another. Comparative analysis with extant methodologies, ranging from convolutional networks to more intricate strategies, enriched our understanding of the current landscape. The variance in performance, even among state-of-the-art models, underscored the inherent challenges in this domain. In essence, this paper serves as both a repository of findings and a clarion call for more nuanced, data-diverse, and adaptable approaches in biometric liveness detection. In the dynamic dance between authenticity and deception, our work offers a blueprint for navigating the evolving rhythms of biometric security.

cs.CV↗

arXiv2023

A Federated Learning Framework for Stenosis Detection

This study explores the use of Federated Learning (FL) for stenosis detection in coronary angiography images (CA). Two heterogeneous datasets from two institutions were considered: Dataset 1 includes 1219 images from 200 patients, which we acquired at the Ospedale Riuniti of Ancona (Italy); Dataset 2 includes 7492 sequential images from 90 patients from a previous study available in the literature. Stenosis detection was performed by using a Faster R-CNN model. In our FL framework, only the weights of the model backbone were shared among the two client institutions, using Federated Averaging (FedAvg) for weight aggregation. We assessed the performance of stenosis detection using Precision (P rec), Recall (Rec), and F1 score (F1). Our results showed that the FL framework does not substantially affects clients 2 performance, which already achieved good performance with local training; for client 1, instead, FL framework increases the performance with respect to local model of +3.76%, +17.21% and +10.80%, respectively, reaching P rec = 73.56, Rec = 67.01 and F1 = 70.13. With such results, we showed that FL may enable multicentric studies relevant to automatic stenosis detection in CA by addressing data heterogeneity from various institutions, while preserving patient privacy.

eess.IV↗

arXiv2023

A store-and-forward cloud-based telemonitoring system for automatic assessing dysarthria evolution in neurological diseases from video-recording analysis

Background and objectives: Patients suffering from neurological diseases may develop dysarthria, a motor speech disorder affecting the execution of speech. Close and quantitative monitoring of dysarthria evolution is crucial for enabling clinicians to promptly implement patient management strategies and maximizing effectiveness and efficiency of communication functions in term of restoring, compensating or adjusting. In the clinical assessment of orofacial structures and functions, at rest condition or during speech and non-speech movements, a qualitative evaluation is usually performed, throughout visual observation. Methods: To overcome limitations posed by qualitative assessments, this work presents a store-and-forward self-service telemonitoring system that integrates, within its cloud architecture, a convolutional neural network (CNN) for analyzing video recordings acquired by individuals with dysarthria. This architecture, called facial landmark Mask RCNN, aims at locating facial landmarks as a prior for assessing the orofacial functions related to speech and examining dysarthria evolution in neurological diseases. Results: When tested on the Toronto NeuroFace dataset, a publicly available annotated dataset of video recordings from patients with amyotrophic lateral sclerosis (ALS) and stroke, the proposed CNN achieved a normalized mean error equal to 1.79 on localizing the facial landmarks. We also tested our system in a real-life scenario on 11 bulbar-onset ALS subjects, obtaining promising outcomes in terms of facial landmark position estimation. Discussion and conclusions: This preliminary study represents a relevant step towards the use of remote tools to support clinicians in monitoring the evolution of dysarthria.

cs.AI↗

arXiv2023

A New Technique of the Virtual Reality Visualization of Complex Volume Images from the Computer Tomography and Magnetic Resonance Imaging

This paper presents a new technique for the virtual reality (VR) visu-alization of complex volume images obtained from computer tomography (CT) and Magnetic Resonance Imaging (MRI) by combining three-dimensional (3D) mesh processing and software coding within the gaming engine. The method operates on real representations of human organs avoiding any structural ap-proximations of the real physiological shape. In order to obtain realistic repre-sentation of the mesh model, geometrical and topological corrections are per-formed on the mesh surface with preserving real shape and geometric structure. Using mathematical intervention on the 3D model and mesh triangulation the second part of our algorithm ensures an automatic construction of new two-dimensional (2D) shapes that represent vector slices along any user chosen di-rection. The final result of our algorithm is developed software application that allows to user complete visual experience and perceptual exploration of real human organs through spatial manipulation of their 3D models. Thus our pro-posed method achieves a threefold effect: i) high definition VR representation of real models of human organs, ii) the real time generated slices of such a model along any directions, and iii) almost unlimited amount of training data for machine learning that is very useful in process of diagnosis. In addition, our developed application also offers significant benefits to educational process by ensuring interactive features and quality perceptual user experience.

cs.MM↗

arXiv2022

Trusted Data Forever: Is AI the Answer?

Archival institutions and programs worldwide work to ensure that the records of governments, organizations, communities, and individuals are preserved for future generations as cultural heritage, as sources of rights, and as vehicles for holding the past accountable and to inform the future. This commitment is guaranteed through the adoption of strategic and technical measures for the long-term preservation of digital assets in any medium and form - textual, visual, or aural. Public and private archives are the largest providers of data big and small in the world and collectively host yottabytes of trusted data, to be preserved forever. Several aspects of retention and preservation, arrangement and description, management and administrations, and access and use are still open to improvement. In particular, recent advances in Artificial Intelligence (AI) open the discussion as to whether AI can support the ongoing availability and accessibility of trustworthy public records. This paper presents preliminary results of the InterPARES Trust AI (I Trust AI) international research partnership, which aims to (1) identify and develop specific AI technologies to address critical records and archives challenges; (2) determine the benefits and risks of employing AI technologies on records and archives; (3) ensure that archival concepts and principles inform the development of responsible AI; and (4) validate outcomes through a conglomerate of case studies and demonstrations.

cs.CY↗

arXiv2022

A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysis

Deep-learning (DL) algorithms are becoming the standard for processing ultrasound (US) fetal images. Despite a large number of survey papers already present in this field, most of them are focusing on a broader area of medical-image analysis or not covering all fetal US DL applications. This paper surveys the most recent work in the field, with a total of 145 research papers published after 2017. Each paper is analyzed and commented on from both the methodology and application perspective. We categorized the papers in (i) fetal standard-plane detection, (ii) anatomical-structure analysis, and (iii) biometry parameter estimation. For each category, main limitations and open issues are presented. Summary tables are included to facilitate the comparison among the different approaches. Publicly-available datasets and performance metrics commonly used to assess algorithm performance are summarized, too. This paper ends with a critical summary of the current state of the art on DL algorithms for fetal US image analysis and a discussion on current challenges that have to be tackled by researchers working in the field to translate the research methodology into the actual clinical practice.

eess.IV↗

arXiv2021

Real-Time Human Pose Estimation on a Smart Walker using Convolutional Neural Networks

Rehabilitation is important to improve quality of life for mobility-impaired patients. Smart walkers are a commonly used solution that should embed automatic and objective tools for data-driven human-in-the-loop control and monitoring. However, present solutions focus on extracting few specific metrics from dedicated sensors with no unified full-body approach. We investigate a general, real-time, full-body pose estimation framework based on two RGB+D camera streams with non-overlapping views mounted on a smart walker equipment used in rehabilitation. Human keypoint estimation is performed using a two-stage neural network framework. The 2D-Stage implements a detection module that locates body keypoints in the 2D image frames. The 3D-Stage implements a regression module that lifts and relates the detected keypoints in both cameras to the 3D space relative to the walker. Model predictions are low-pass filtered to improve temporal consistency. A custom acquisition method was used to obtain a dataset, with 14 healthy subjects, used for training and evaluating the proposed framework offline, which was then deployed on the real walker equipment. An overall keypoint detection error of 3.73 pixels for the 2D-Stage and 44.05mm for the 3D-Stage were reported, with an inference time of 26.6ms when deployed on the constrained hardware of the walker. We present a novel approach to patient monitoring and data-driven human-in-the-loop control in the context of smart walkers. It is able to extract a complete and compact body representation in real-time and from inexpensive sensors, serving as a common base for downstream metrics extraction solutions, and Human-Robot interaction applications. Despite promising results, more data should be collected on users with impairments, to assess its performance as a rehabilitation tool in real-world scenarios.

cs.CV↗

arXiv2020

Comparison of different CNNs for breast tumor classification from ultrasound images

Breast cancer is one of the deadliest cancer worldwide. Timely detection could reduce mortality rates. In the clinical routine, classifying benign and malignant tumors from ultrasound (US) imaging is a crucial but challenging task. An automated method, which can deal with the variability of data is therefore needed. In this paper, we compared different Convolutional Neural Networks (CNNs) and transfer learning methods for the task of automated breast tumor classification. The architectures investigated in this study were VGG-16 and Inception V3. Two different training strategies were investigated: the first one was using pretrained models as feature extractors and the second one was to fine-tune the pre-trained models. A total of 947 images were used, 587 corresponded to US images of benign tumors and 360 with malignant tumors. 678 images were used for the training and validation process, while 269 images were used for testing the models. Accuracy and Area Under the receiver operating characteristic Curve (AUC) were used as performance metrics. The best performance was obtained by fine tuning VGG-16, with an accuracy of 0.919 and an AUC of 0.934. The obtained results open the opportunity to further investigation with a view of improving cancer detection.

eess.IV↗

arXiv2020

Preterm infants' pose estimation with spatio-temporal features

Objective: Preterm infants' limb monitoring in neonatal intensive care units (NICUs) is of primary importance for assessing infants' health status and motor/cognitive development. Herein, we propose a new approach to preterm infants' limb pose estimation that features spatio-temporal information to detect and track limb joints from depth videos with high reliability. Methods: Limb-pose estimation is performed using a deep-learning framework consisting of a detection and a regression convolutional neural network (CNN) for rough and precise joint localization, respectively. The CNNs are implemented to encode connectivity in the temporal direction through 3D convolution. Assessment of the proposed framework is performed through a comprehensive study with sixteen depth videos acquired in the actual clinical practice from sixteen preterm infants (the babyPose dataset). Results: When applied to pose estimation, the median root mean squared distance, computed among all limbs, between the estimated and the ground-truth pose was 9.06 pixels, overcoming approaches based on spatial features only (11.27pixels). Conclusion: Results showed that the spatio-temporal features had a significant influence on the pose-estimation performance, especially in challenging cases (e.g., homogeneous image intensity). Significance: This paper significantly enhances the state of art in automatic assessment of preterm infants' health status by introducing the use of spatio-temporal features for limb detection and tracking, and by being the first study to use depth videos acquired in the actual clinical practice for limb-pose estimation. The babyPose dataset has been released as the first annotated dataset for infants' pose estimation.

cs.CV↗

arXiv2019

Preterm infants' limb-pose estimation from depth images using convolutional neural networks

Preterm infants' limb-pose estimation is a crucial but challenging task, which may improve patients' care and facilitate clinicians in infant's movements monitoring. Work in the literature either provides approaches to whole-body segmentation and tracking, which, however, has poor clinical value, or retrieve a posteriori limb pose from limb segmentation, increasing computational costs and introducing inaccuracy sources. In this paper, we address the problem of limb-pose estimation under a different point of view. We proposed a 2D fully-convolutional neural network for roughly detecting limb joints and joint connections, followed by a regression convolutional neural network for accurate joint and joint-connection position estimation. Joints from the same limb are then connected with a maximum bipartite matching approach. Our analysis does not require any prior modeling of infants' body structure, neither any manual interventions. For developing and testing the proposed approach, we built a dataset of four videos (video length = 90 s) recorded with a depth sensor in a neonatal intensive care unit (NICU) during the actual clinical practice, achieving median root mean square distance [pixels] of 10.790 (right arm), 10.542 (left arm), 8.294 (right leg), 11.270 (left leg) with respect to the ground-truth limb pose. The idea of estimating limb pose directly from depth images may represent a future paradigm for addressing the problem of preterm-infants' movement monitoring and offer all possible support to clinicians in NICUs.

cs.CV↗

arXiv2015

Shopper Analytics: a customer activity recognition system using a distributed RGB-D camera network

The aim of this paper is to present an integrated system consisted of a RGB-D camera and a software able to monitor shoppers in intelligent retail environments. We propose an innovative low cost smart system that can understand the shoppers' behavior and, in particular, their interactions with the products in the shelves, with the aim to develop an automatic RGB-D technique for video analysis. The system of cameras detects the presence of people and univocally identifies them. Through the depth frames, the system detects the interactions of the shoppers with the products on the shelf and determines if a product is picked up or if the product is taken and then put back and finally, if there is not contact with the products. The system is low cost and easy to install, and experimental results demonstrated that its performances are satisfactory also in real environments.

cs.CV↗

arXiv2014

Security issues for data sharing and service interoperability in eHealth systems: the Nu.Sa. test bed

The aim of the Nu.Sa. project is the definition of national level data standards to collect data coming from General Practitioners' Electronic Health Records and to allow secure data sharing between them. This paper introduces the Nu.Sa. framework and is mainly focused on security issues. A solution for secure data sharing and service interoperability is presented and implemented in the actual system used around Italy. The solution is strongly focused on privacy and correct data sharing with a complete set of tools devoted to authorization, encryption and decryption in a data sharing environment and a distributed architecture. The implemented system with more than one year of experiences in thousands of test cases shows a good feasibility of the approach and a future scalability in a cloud based architecture.

cs.CR↗