The document discusses face recognition techniques. It begins with an introduction and taxonomy of face recognition methods. The main challenges of face recognition are identified as accounting for inter-class similarities between faces and intra-class variability within faces, due to factors like head pose, illumination, expression, accessories, and aging. It then outlines the typical pattern recognition architecture used in face recognition systems involving image capture, face representation, feature extraction, classification, and results. Dimensionality reduction techniques like Eigenfaces and Fisherfaces are also discussed.
Pose and Illumination in Face Recognition Using Enhanced Gabor LBP & PCA IJMER
This paper presents the face recognition based on Enhanced GABOR LBP and PCA. Some
of the challenges in face recognition are occlusion, pose and illumination .In this paper, we are more
focused on varying pose and illumination. We divided this algorithm into five stages. First stage finds
the fiducial points on face using Gabor filter bank as this filter is well known for illumination
compensation. Second stage applies the morphological techniques for reduce useless fiducial points.
Third stage applies the LBP on reduced fiducial points with neighborhood pixel for improving the pose
variation. Forth stage uses PCA to detect the best variance points which are necessary to characterize
the training images. The last recognition stage includes finding the Euclidean norm of the feature
weight vectors with the test weight vector. In this project, we used 20 images of 20 different persons
from ORL database for training. For testing, we used images with varying illumination, pose and
occluded images of the same training
A Novel Approach for Detection of Routes with Misbehaving Nodes in MANETsIDES Editor
Network nodes in MANET’s are free to move randomly.
Therefore, the network topology may change rapidly.
Routing protocol for MANET’s are used for delivery of data
packets from source to the desired destination, Routing protocols
are also designed based on the assumption that all the
participating nodes are fully cooperative. However, due to the
scarcely available battery based energy, node behaviours may
exist. One such routing misbehaviours is that some nodes may
be selfish by participating in route discovery and maintenance
process, but refuse to forward the packet in order to save its
energy. To solve this problem we propose a reputation based
scheme where the watch dog uses a passive overhearing of
nodes and assign a value to it as an appreciation or add nuggets
to them. In this proposal, nodes with highest value are
highly recommended for data forwarding and allow nodes to
avoid the use of misbehaving nodes in future route selection.
AdHoc On Demand Distance vector routing protocol may be
used to get the recommendation details of the node intended
to forward the packet from the neighbouring nodes. This paper
proposes a novel method to mitigate the route with misbehaving
nodes and also suggests a way to find if any intruder is
present in the cluster of participating nodes using security
aware AODV protocol.
Ever wondered how computers recognize and track human faces? Netlighter Saksham Gautam held a talk at Netlight Grand Edge Data Munich and dissected this seemingly complex problem of object recognition and tracking into digestible pieces. You will get a good understanding of what happens behind the scenes when, for instance, you walk through automatic passport control at the airport, or how Google's self driven car 'sees' the road signs. We will see how these generic algorithms and techniques can be applied to other machine learning and pattern recognition problems as well.
Face recognition across non uniform motion blur, illumination, and posePvrtechnologies Nellore
The document proposes a method for face recognition in the presence of non-uniform motion blur from hand-held cameras. It models a blurred face as a convex combination of geometrically transformed gallery images. It develops an algorithm using the assumption of sparse camera motion and an l1-norm constraint. The framework is extended to handle illumination variations by exploiting the bi-convex set of images from blurring and illumination changes. The method is also extended to account for pose variations and uses a multi-scale implementation for efficient computation and memory usage.
3-D Face Recognition Using Improved 3D Mixed TransformCSCJournals
This paper deals with the using of Improved 3D Mixed Transform (3D-IMT) for face recognition problem. The mixed transform consists; Fourier based 3D radon transform plus 1-D Wavelet transform (which is also known as 3D Ridgelet transform). The Mixed Transform is improved by using Particle swarm optimization ( PSO) , the improvement involves the selection of the best of directions for smart rectangle-to-polar transform as a part of the 3D Radon Transformation. The 3D-IMT is applied to the 3D representation of face images, and yields a few number of features, these features is projected into the maximized projection that achieves good recognition rate using the Linear Discriminant Analysis (LDA).
3D Face Recognition Method Using 2DPCAEuclidean Distance ClassificationIDES Editor
In this paper, we present a 3d face recognition method
that is robust to changes in facial expressions. Instead of
locating many feature points, we just need to locate the nose
tip as a reference point. After finding this reference point,
pictures are converted to a standard size. Two dimensional
principle component analysis (2DPCA) is employed to obtain
features Matrix vectors. Finally Euclidean distance method is
employed for classifying and comparison of the features.
Experimental results implemented on CASIA 3D face database
which including 123 individuals in total, demonstrate that
our proposed method achieves up to 98% recognition accuracy
with respect to pose variation.
A New Algorithm for Human Face Detection Using Skin Color ToneIOSR Journals
This document presents a new algorithm for human face detection using skin color tone. The algorithm first resizes images and separates them into R, G, and B color bands, which are then transformed into the YCbCr color space and further into the YC'bC'r skin color tone space. Morphological processing is applied to make the images more accurate before determining the face area through projection. Experimental results show the algorithm can localize human faces in images with 92.69% accuracy by classifying pixels as skin or non-skin based on their YCbCr values.
The document discusses face recognition techniques. It begins with an introduction and taxonomy of face recognition methods. The main challenges of face recognition are identified as accounting for inter-class similarities between faces and intra-class variability within faces, due to factors like head pose, illumination, expression, accessories, and aging. It then outlines the typical pattern recognition architecture used in face recognition systems involving image capture, face representation, feature extraction, classification, and results. Dimensionality reduction techniques like Eigenfaces and Fisherfaces are also discussed.
Pose and Illumination in Face Recognition Using Enhanced Gabor LBP & PCA IJMER
This paper presents the face recognition based on Enhanced GABOR LBP and PCA. Some
of the challenges in face recognition are occlusion, pose and illumination .In this paper, we are more
focused on varying pose and illumination. We divided this algorithm into five stages. First stage finds
the fiducial points on face using Gabor filter bank as this filter is well known for illumination
compensation. Second stage applies the morphological techniques for reduce useless fiducial points.
Third stage applies the LBP on reduced fiducial points with neighborhood pixel for improving the pose
variation. Forth stage uses PCA to detect the best variance points which are necessary to characterize
the training images. The last recognition stage includes finding the Euclidean norm of the feature
weight vectors with the test weight vector. In this project, we used 20 images of 20 different persons
from ORL database for training. For testing, we used images with varying illumination, pose and
occluded images of the same training
A Novel Approach for Detection of Routes with Misbehaving Nodes in MANETsIDES Editor
Network nodes in MANET’s are free to move randomly.
Therefore, the network topology may change rapidly.
Routing protocol for MANET’s are used for delivery of data
packets from source to the desired destination, Routing protocols
are also designed based on the assumption that all the
participating nodes are fully cooperative. However, due to the
scarcely available battery based energy, node behaviours may
exist. One such routing misbehaviours is that some nodes may
be selfish by participating in route discovery and maintenance
process, but refuse to forward the packet in order to save its
energy. To solve this problem we propose a reputation based
scheme where the watch dog uses a passive overhearing of
nodes and assign a value to it as an appreciation or add nuggets
to them. In this proposal, nodes with highest value are
highly recommended for data forwarding and allow nodes to
avoid the use of misbehaving nodes in future route selection.
AdHoc On Demand Distance vector routing protocol may be
used to get the recommendation details of the node intended
to forward the packet from the neighbouring nodes. This paper
proposes a novel method to mitigate the route with misbehaving
nodes and also suggests a way to find if any intruder is
present in the cluster of participating nodes using security
aware AODV protocol.
Ever wondered how computers recognize and track human faces? Netlighter Saksham Gautam held a talk at Netlight Grand Edge Data Munich and dissected this seemingly complex problem of object recognition and tracking into digestible pieces. You will get a good understanding of what happens behind the scenes when, for instance, you walk through automatic passport control at the airport, or how Google's self driven car 'sees' the road signs. We will see how these generic algorithms and techniques can be applied to other machine learning and pattern recognition problems as well.
Face recognition across non uniform motion blur, illumination, and posePvrtechnologies Nellore
The document proposes a method for face recognition in the presence of non-uniform motion blur from hand-held cameras. It models a blurred face as a convex combination of geometrically transformed gallery images. It develops an algorithm using the assumption of sparse camera motion and an l1-norm constraint. The framework is extended to handle illumination variations by exploiting the bi-convex set of images from blurring and illumination changes. The method is also extended to account for pose variations and uses a multi-scale implementation for efficient computation and memory usage.
3-D Face Recognition Using Improved 3D Mixed TransformCSCJournals
This paper deals with the using of Improved 3D Mixed Transform (3D-IMT) for face recognition problem. The mixed transform consists; Fourier based 3D radon transform plus 1-D Wavelet transform (which is also known as 3D Ridgelet transform). The Mixed Transform is improved by using Particle swarm optimization ( PSO) , the improvement involves the selection of the best of directions for smart rectangle-to-polar transform as a part of the 3D Radon Transformation. The 3D-IMT is applied to the 3D representation of face images, and yields a few number of features, these features is projected into the maximized projection that achieves good recognition rate using the Linear Discriminant Analysis (LDA).
3D Face Recognition Method Using 2DPCAEuclidean Distance ClassificationIDES Editor
In this paper, we present a 3d face recognition method
that is robust to changes in facial expressions. Instead of
locating many feature points, we just need to locate the nose
tip as a reference point. After finding this reference point,
pictures are converted to a standard size. Two dimensional
principle component analysis (2DPCA) is employed to obtain
features Matrix vectors. Finally Euclidean distance method is
employed for classifying and comparison of the features.
Experimental results implemented on CASIA 3D face database
which including 123 individuals in total, demonstrate that
our proposed method achieves up to 98% recognition accuracy
with respect to pose variation.
A New Algorithm for Human Face Detection Using Skin Color ToneIOSR Journals
This document presents a new algorithm for human face detection using skin color tone. The algorithm first resizes images and separates them into R, G, and B color bands, which are then transformed into the YCbCr color space and further into the YC'bC'r skin color tone space. Morphological processing is applied to make the images more accurate before determining the face area through projection. Experimental results show the algorithm can localize human faces in images with 92.69% accuracy by classifying pixels as skin or non-skin based on their YCbCr values.
1. The document summarizes research on improving a single person pose recognition and tracking system using computer vision techniques. The goal is to better detect body parts and recognize poses in real-time using a single camera.
2. Key aspects of the system include using a mixture of Gaussians model for background subtraction, and a particle filter for tracking the torso and head. Hand detection is improved by combining skin color detection with the human blob silhouette.
3. The research aims to improve pose recognition performance by classifying "non-poses" - poses that are different from the predefined poses. Experiments show that increasing the dataset size and adding a "non-pose" class leads to better detection results.
Face Recognition Using Neural Network Based Fourier Gabor Filters & Random Pr...CSCJournals
Face detection and recognition has many applications in a variety of fields such as authentication, security, video surveillance and human interaction systems. In this paper, we present a neural network system for face recognition. Feature vector based on Fourier Gabor filters is used as input of our classifier, which is a Back Propagation Neural Network (BPNN). The input vector of the network will have large dimension, to reduce its feature subspace we investigate the use of the Random Projection as method of dimensionality reduction. Theory and experiment indicates the robustness of our solution.
Cryptography using artificial neural networkMahira Banu
This document proposes using artificial neural networks for cryptography. It describes using a backpropagation neural network for decryption, where the network is trained on encrypted-decrypted message pairs. Boolean algebra is used for encryption, permuting messages and "doping" with additional bits. The neural network can then be used as a public key for decryption, with a private key for encryption. Simulation results showed the neural network approach weakened key guessing compared to other methods.
Face recognition is a type of biometric software that uses analysis of facial patterns to identify individuals. It has various applications including security, law enforcement, and social media photo tagging. The technology works by measuring nodal points on faces like eye and nose position to create unique numerical faceprints for identification and verification. While effective, face recognition depends on clear images and has limitations with expressions, lighting, or obscured faces. It is increasingly being implemented in areas like access control, immigration, and banking due to lower costs.
This document discusses a novel approach to face recognition using stereo matching algorithms. It begins by introducing face recognition and its challenges, such as handling pose and illumination variations. It then describes the key components of the face recognition system, including detecting facial landmarks, generating a gallery image, and calculating matching costs to identify similarities between images. The document focuses on a stereo matching approach that supports good correspondence between images and maximizes a cost function. It evaluates the performance of this method on real images, finding a recognition rate of 98.27% compared to other approaches.
Medical multi signal signature recognition applied Cardiac DiagnosisTaleb ALASHKAR
This MSc thesis falls in the field of features extraction from ECG signals in order to build automatic analysis system to help cardiologist in their work. In this work we presented new approach for detecting features from ECG signals depending on nonlinear derivative scheme for one dimension 1D NLFS. We started by proposing a theatrical model for exploiting this scheme to detect features from free of noise synthetic ECG signal, then we developed this model to overcome challenges that appears in real noisy ECG signals to build new approach. We started detection by QRS complex peak detection as a first step, and then we extended the approach to detect the onset and end for P and T-waves in the beat. Performance evaluation of our approach have been conducted on records from MIT QT Standard Database that has manual annotations by experts for testing the sensitivity, positive predictivity, mean error and standard deviation for detection of each feature separately. The results obtained from this single lead-based ECG , analysis approach are promising especially in P and T-waves delineation. Because it is the first use of 1D NLFS approach in the field of feature extraction, there is a big chance to enhance the performance in the future and to extend the application of this approach for more type of signals.
Then we tested how much this approach is robust against noise, by applying this approach on synthetic ECG signals with different level of noise. It gives good performance against noise.
Point Placement Algorithms: An Experimental StudyCSCJournals
The point location problem is to determine the position of n distinct points on a line, up to translation and reflection by the fewest possible pairwise (adversarial) distance queries. In this paper we report on an experimental study of a number of deterministic point placement algorithms and an incremental randomized algorithm, with the goal of obtaining a greater insight into the practical utility of these algorithms, particularly of the randomized one.
This document summarizes an approach for object recognition and 6-DoF pose estimation from RGB-D images. It first segments the scene to isolate objects from background surfaces like tables. It then clusters the remaining points into individual objects. For each object model, it generates synthetic views from different angles and extracts a global feature descriptor combining geometry and color information. During recognition, it extracts descriptors from segmented objects, finds nearest matches in the training database, and estimates the object and its pose. Experimental results demonstrate high recognition rates and pose accuracy, even under occlusion.
Image Enhancement by Image Fusion for Crime InvestigationCSCJournals
This document proposes a method for image enhancement through image fusion for crime investigation applications. It summarizes existing image enhancement techniques like histogram equalization and presents their limitations. It then describes the proposed method which involves constructing an image pyramid and performing a wavelet transformation on input images. The pyramid and wavelet transformed images are then fused to generate an enhanced output image with improved contrast and information content. Experimental results on a surveillance camera image show that the proposed fusion scheme provides better perception for human visual analysis compared to traditional enhancement techniques.
This document provides an overview of face recognition technology. It discusses 2D and 3D facial recognition, how the technology works by measuring facial features to create a unique face print, hardware and software requirements, advantages like identifying repeat offenders, and applications in security, multimedia, and law enforcement. The conclusion states that while progress has been made, continued work is needed to develop more accurate systems.
Face recognition is a technique for identifying or verifying individuals using their facial features. It involves extracting features from images, selecting subsets of important features, and matching feature sets to identify faces. 2D recognition uses image features while 3D recognition uses geometric facial features, improving accuracy over 2D. Face recognition has applications in security, biometrics, law enforcement, and advertising. Key algorithms include PCA, LDA, neural networks, and recently deep learning methods.
The document discusses an 8K extremely high resolution camera system seminar report submitted by Prejith Pavanan in partial fulfillment of a Bachelor of Technology degree. It provides an overview of two 8K camera systems - one using three 33-megapixel image sensors, and one using a more practical four-sensor imaging method with 8-megapixel sensors. The report covers topics such as the history of cameras, display resolutions including 4K and 8K, digital cinematography, and details of the two 8K camera prototypes including their designs, image sensors used, and capabilities.
Efficient Point Cloud Pre-processing using The Point Cloud LibraryCSCJournals
Robotics, video games, environmental mapping and medical are some of the fields that use 3D data processing. In this paper we propose a novel optimization approach for the open source Point Cloud Library (PCL) that is frequently used for processing 3D data. Three main aspects of the PCL are discussed: point cloud creation from disparity of color image pairs; voxel grid downsample filtering to simplify point clouds; and passthrough filtering to adjust the size of the point cloud. Additionally, OpenGL shader based rendering is examined. An optimization technique based on CPU cycle measurement is proposed and applied in order to optimize those parts of the pre-processing chain where measured performance is slowest. Results show that with optimized modules the performance of the pre-processing chain has increased 69 fold.
To identify and simulate conventional type of disturbance on the overhead transmission line by using PSCAD / EMTDC software package
To develop mathematical model for various type of disturbance on overhead transmission line.
To develop a smart algorithm for fault detection using Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO).
Il progetto MeB, frutto della collaborazione tra la Scuola Normale Superiore e i Musei del Bargello, ha avuto come principale obiettivo quello di sviluppare una ricerca condivisa di natura applicativa, finalizzata alla modernizzazione dei processi di analisi scientifica e studio storico-artistico della produzione metallica di piccole dimensioni d’età moderna e all’elaborazione, anche attraverso scansioni laser 3D, di soluzioni operative di potenziale interesse per analoghi contesti culturali.
1. The document summarizes research on improving a single person pose recognition and tracking system using computer vision techniques. The goal is to better detect body parts and recognize poses in real-time using a single camera.
2. Key aspects of the system include using a mixture of Gaussians model for background subtraction, and a particle filter for tracking the torso and head. Hand detection is improved by combining skin color detection with the human blob silhouette.
3. The research aims to improve pose recognition performance by classifying "non-poses" - poses that are different from the predefined poses. Experiments show that increasing the dataset size and adding a "non-pose" class leads to better detection results.
Face Recognition Using Neural Network Based Fourier Gabor Filters & Random Pr...CSCJournals
Face detection and recognition has many applications in a variety of fields such as authentication, security, video surveillance and human interaction systems. In this paper, we present a neural network system for face recognition. Feature vector based on Fourier Gabor filters is used as input of our classifier, which is a Back Propagation Neural Network (BPNN). The input vector of the network will have large dimension, to reduce its feature subspace we investigate the use of the Random Projection as method of dimensionality reduction. Theory and experiment indicates the robustness of our solution.
Cryptography using artificial neural networkMahira Banu
This document proposes using artificial neural networks for cryptography. It describes using a backpropagation neural network for decryption, where the network is trained on encrypted-decrypted message pairs. Boolean algebra is used for encryption, permuting messages and "doping" with additional bits. The neural network can then be used as a public key for decryption, with a private key for encryption. Simulation results showed the neural network approach weakened key guessing compared to other methods.
Face recognition is a type of biometric software that uses analysis of facial patterns to identify individuals. It has various applications including security, law enforcement, and social media photo tagging. The technology works by measuring nodal points on faces like eye and nose position to create unique numerical faceprints for identification and verification. While effective, face recognition depends on clear images and has limitations with expressions, lighting, or obscured faces. It is increasingly being implemented in areas like access control, immigration, and banking due to lower costs.
This document discusses a novel approach to face recognition using stereo matching algorithms. It begins by introducing face recognition and its challenges, such as handling pose and illumination variations. It then describes the key components of the face recognition system, including detecting facial landmarks, generating a gallery image, and calculating matching costs to identify similarities between images. The document focuses on a stereo matching approach that supports good correspondence between images and maximizes a cost function. It evaluates the performance of this method on real images, finding a recognition rate of 98.27% compared to other approaches.
Medical multi signal signature recognition applied Cardiac DiagnosisTaleb ALASHKAR
This MSc thesis falls in the field of features extraction from ECG signals in order to build automatic analysis system to help cardiologist in their work. In this work we presented new approach for detecting features from ECG signals depending on nonlinear derivative scheme for one dimension 1D NLFS. We started by proposing a theatrical model for exploiting this scheme to detect features from free of noise synthetic ECG signal, then we developed this model to overcome challenges that appears in real noisy ECG signals to build new approach. We started detection by QRS complex peak detection as a first step, and then we extended the approach to detect the onset and end for P and T-waves in the beat. Performance evaluation of our approach have been conducted on records from MIT QT Standard Database that has manual annotations by experts for testing the sensitivity, positive predictivity, mean error and standard deviation for detection of each feature separately. The results obtained from this single lead-based ECG , analysis approach are promising especially in P and T-waves delineation. Because it is the first use of 1D NLFS approach in the field of feature extraction, there is a big chance to enhance the performance in the future and to extend the application of this approach for more type of signals.
Then we tested how much this approach is robust against noise, by applying this approach on synthetic ECG signals with different level of noise. It gives good performance against noise.
Point Placement Algorithms: An Experimental StudyCSCJournals
The point location problem is to determine the position of n distinct points on a line, up to translation and reflection by the fewest possible pairwise (adversarial) distance queries. In this paper we report on an experimental study of a number of deterministic point placement algorithms and an incremental randomized algorithm, with the goal of obtaining a greater insight into the practical utility of these algorithms, particularly of the randomized one.
This document summarizes an approach for object recognition and 6-DoF pose estimation from RGB-D images. It first segments the scene to isolate objects from background surfaces like tables. It then clusters the remaining points into individual objects. For each object model, it generates synthetic views from different angles and extracts a global feature descriptor combining geometry and color information. During recognition, it extracts descriptors from segmented objects, finds nearest matches in the training database, and estimates the object and its pose. Experimental results demonstrate high recognition rates and pose accuracy, even under occlusion.
Image Enhancement by Image Fusion for Crime InvestigationCSCJournals
This document proposes a method for image enhancement through image fusion for crime investigation applications. It summarizes existing image enhancement techniques like histogram equalization and presents their limitations. It then describes the proposed method which involves constructing an image pyramid and performing a wavelet transformation on input images. The pyramid and wavelet transformed images are then fused to generate an enhanced output image with improved contrast and information content. Experimental results on a surveillance camera image show that the proposed fusion scheme provides better perception for human visual analysis compared to traditional enhancement techniques.
This document provides an overview of face recognition technology. It discusses 2D and 3D facial recognition, how the technology works by measuring facial features to create a unique face print, hardware and software requirements, advantages like identifying repeat offenders, and applications in security, multimedia, and law enforcement. The conclusion states that while progress has been made, continued work is needed to develop more accurate systems.
Face recognition is a technique for identifying or verifying individuals using their facial features. It involves extracting features from images, selecting subsets of important features, and matching feature sets to identify faces. 2D recognition uses image features while 3D recognition uses geometric facial features, improving accuracy over 2D. Face recognition has applications in security, biometrics, law enforcement, and advertising. Key algorithms include PCA, LDA, neural networks, and recently deep learning methods.
The document discusses an 8K extremely high resolution camera system seminar report submitted by Prejith Pavanan in partial fulfillment of a Bachelor of Technology degree. It provides an overview of two 8K camera systems - one using three 33-megapixel image sensors, and one using a more practical four-sensor imaging method with 8-megapixel sensors. The report covers topics such as the history of cameras, display resolutions including 4K and 8K, digital cinematography, and details of the two 8K camera prototypes including their designs, image sensors used, and capabilities.
Efficient Point Cloud Pre-processing using The Point Cloud LibraryCSCJournals
Robotics, video games, environmental mapping and medical are some of the fields that use 3D data processing. In this paper we propose a novel optimization approach for the open source Point Cloud Library (PCL) that is frequently used for processing 3D data. Three main aspects of the PCL are discussed: point cloud creation from disparity of color image pairs; voxel grid downsample filtering to simplify point clouds; and passthrough filtering to adjust the size of the point cloud. Additionally, OpenGL shader based rendering is examined. An optimization technique based on CPU cycle measurement is proposed and applied in order to optimize those parts of the pre-processing chain where measured performance is slowest. Results show that with optimized modules the performance of the pre-processing chain has increased 69 fold.
To identify and simulate conventional type of disturbance on the overhead transmission line by using PSCAD / EMTDC software package
To develop mathematical model for various type of disturbance on overhead transmission line.
To develop a smart algorithm for fault detection using Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO).
Il progetto MeB, frutto della collaborazione tra la Scuola Normale Superiore e i Musei del Bargello, ha avuto come principale obiettivo quello di sviluppare una ricerca condivisa di natura applicativa, finalizzata alla modernizzazione dei processi di analisi scientifica e studio storico-artistico della produzione metallica di piccole dimensioni d’età moderna e all’elaborazione, anche attraverso scansioni laser 3D, di soluzioni operative di potenziale interesse per analoghi contesti culturali.
ebook del progetto Carbon Footprint, attraverso le arti digitali finanziato dal Miur e realizzato dalla Fondazione Mondo Digitale in collaborazione lo Sapienza Università di Roma in occasione del Media Art Festival 2017
1. Electronic for Security and Cultural HEritage Research - LABoratory
PhD Student: Lorenzo COZZELLA
Tutor: Prof. Giuseppe SCHIRRIPA SPAGNOLO
2. La Falsificazione
La cultura della copia e delle falsificazioni
fa parte della nostra storia e della nostra
cultura. Inoltre copiare è parte integrale
del processo di apprendimento.
La foto ritrae la "Diana di
Versailles", attualmente custodita al Louvre di
Parigi. La statua venne realizzata tra il primo ed
il secondo secolo d.C. E' una copia romana
dell'originale greca attribuita a Leocare. Fu
donata da Papa Paolo IV al re di Francia Enrico
II, nel sedicesimo secolo.
Nota: I romani hanno realizzato in marmo numerose copie di statue greche
in bronzo. Grazie a queste copie romane è stato possibile ammirare
splendide statue greche oggi andate perdute, sia per l'estrema antichità
sia, soprattutto, perché il bronzo veniva fuso per essere riutilizzato.
- Electronic for Security and Cultural HEritage Research - LABoratory
3. Hylemetria
Poiché i sistemi di identificazione biometrica hanno dato
ottimi risultati, viene spontaneo chiedersi se criteri simili
possano essere utilizzati per identificare oggetti
inanimati, come ad esempio i manufatti storico-artistici.
Ovviamente l’identificazione risulta possibile se si riescono
ad individuare caratteristiche specifiche di un particolare
manufatto. In altre parole, caratteristiche
uniche, irriproducibili e immutabili potrebbero assegnare
un’identità al manufatto.
Sfruttando le caratteristiche, irriproducibili e
immutabili, proprie di un manufatto, è possibile un
meccanismo di identificazione che associ, in modo
univoco, un “certificato di autenticità” ad un particolare
manufatto; sia esso un’opera d’arte, un mobile antico, una
gemma preziosa, una moneta antica, ecc.
- Electronic for Security and Cultural HEritage Research - LABoratory
4. ... continua Hylemetria
Considerando che Aristotele ha chiamato hyle (űλη)
la materia inanimata (ciò di cui un oggetto è
fatto), in analogia all’identificazione
biometrica, chiameremo hylemetria la procedura di
identificazione di un oggetto inanimato.
Scopo dell’identificazione hylemetria è quello di acquisire
delle caratteristiche casuali e irriproducibili, processarle
per estrarne le informazioni caratteristiche distintive
(template), rappresentarle tramite una stringa firmabile
digitalmente.
- Electronic for Security and Cultural HEritage Research - LABoratory
5. Biometria vs. Hylemetria
Biometria Hylemetria
riconoscimento utilizzo delle prorietà
caratteristiche fisiche microscopiche di un
dell’individuo oggetto
Geometria Impronte digitali Fibre della Rumore di
della mano Iride carta stampa
- Electronic for Security and Cultural HEritage Research - LABoratory
6. Applicazione Hylemetrica su una litografia
- Electronic for Security and Cultural HEritage Research - LABoratory
7. ... continua Applicazione Hylemetrica su una litografia
- Electronic for Security and Cultural HEritage Research - LABoratory
8. ... continua Applicazione Hylemetrica su una litografia
- Electronic for Security and Cultural HEritage Research - LABoratory
9. ... continua Applicazione Hylemetrica su Banconota
random position of the
metallic color fibers
- Electronic for Security and Cultural HEritage Research - LABoratory
10. ... continua Applicazione Hylemetrica su Banconota
- Electronic for Security and Cultural HEritage Research - LABoratory
11. Applicazione Hylemetrica su contenitore per farmaci
- Electronic for Security and Cultural HEritage Research - LABoratory
12. ... continua Applicazione Hylemetrica su contenitore
per farmaci
- Electronic for Security and Cultural HEritage Research - LABoratory
13. Pubblicazioni
• G. Schirripa Spagnolo, C. Simonetti, L. Cozzella, IR Fringe Projection for 3D Face
Recognition, Proceeding of International Conference on Advanced Phase
Measurement Methods in Optics and Imaging, Locarno, Maggio 2010
• G. Schirripa Spagnolo, C. Simonetti, L. Cozzella, Autenticazione di documenti di
identità tramite watermarking fragile, Safety and Security Maggio 2010
• G. Schirripa Spagnolo, L. Cozzella, C. Simonetti, Banconote verifica tramite
approccio biometrico, ICT Security – Gennaio 2011
• G. Schirripa Spagnolo, L. Cozzella, C. Simonetti, Banknote security using a
biometric-like technique: a hylemetric approach, Meas. Sci. Technol., Vol. 21 (2010)
055501 (8pp), doi:10.1088/0957-0233/21/5/055501, Maggio 2010
• G. Schirripa Spagnolo, L. Cozzella, C. Simonetti, Currency verification by a 2D
infrared barcode, Meas. Sci. Technol. Vol. 21 (2010) 107002 (5pp),
doi:10.1088/0957-0233/21/10/107002
• L. Cozzella, C. Simonetti, G. Schirripa Spagnolo, Hylemetry versus Biometry: a
new method to certificate the lithography authenticity, Proc. SPIE 8084, 80840S
(2011); doi:10.1117/12.889387 (Maggio 2011)
• L. Cozzella, C. Simonetti, D. Papalillo, G. Schirripa Spagnolo, Designing of
binary diffractive optical elements for beams performing, Proc. SPIE 8306, 83060N-
1(8); doi:10.1117/12.912311 (Agosto 2011)
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