Multiple constraints to compute optical flow

The computation of the optical flow field from an image sequence requires the definition of constraints on the temporal change of image features. In this paper, we consider the implications of using multiple constraints in the computational schema. In the first step, it is shown that differential constraints correspond to an implicit feature tracking. Therefore, the best results (either in terms of measurement accuracy, and speed in the computation) are obtained by selecting and applying the constraints which are best “tuned” to the particular image feature under consideration. Considering also multiple image points not only allows us to obtain a (locally) better estimate of the velocity field, but also to detect erroneous measurements due to discontinuities in the velocity field. Moreover, by hypothesizing a constant acceleration motion model, also the derivatives of the optical flow are computed. Several experiments are presented from real image sequences

On the use of SIFT features for face authentication

Several pattern recognition and classification techniques have been applied to the biometrics domain. Among them, an interesting technique is the Scale Invariant Feature Transform (SIFT), originally devised for object recognition. Even if SIFT features have emerged as a very powerful image descriptors, their employment in face analysis context has never been systematically investigated. This paper investigates the application of the SIFT approach in the context of face authentication. In order to determine the real potential and applicability of the method, different matching schemes are proposed and tested using the BANCA database and protocol, showing promising results

Understanding critical factors in gender recognition

Gender classification is a task of paramount importance in face recognition research, and it is potentially useful in a large set of applications. In this paper we investigate the gender classification problem by an extended empirical analysis on the Face Recognition Grand Challenge version 2.0 dataset (FRGC2.0). We propose challenging experimental protocols over the dimensions of FRGC2.0 – i.e., subject, face expression, race, controlled or uncontrolled environment. We evaluate our protocols with respect to several classification algorithms, and processing different types of features, like Gabor and LBP. Our results show that gender classification is independent from factors like the race of the subject, face expressions, and variations of controlled illumination conditions. We also report that Gabor features seem to be more robust than LBPs in the case of uncontrolled environment

Robust multi-modal and multi-unit feature level fusion of face and iris biometrics

Multi-biometrics has recently emerged as a mean of more robust and effcient personal verification and identification. Exploiting information from multiple sources at various levels i.e., feature, score, rank or decision, the false acceptance and rejection rates can be considerably reduced. Among all, feature level fusion is relatively an understudied problem. This paper addresses the feature level fusion for multi-modal and multi-unit sources of information. For multi-modal fusion the face and iris biometric traits are considered, while the multi-unit fusion is applied to merge the data from the left and right iris images. The proposed approach computes the SIFT features from both biometric sources, either multi- modal or multi-unit. For each source, the extracted SIFT features are selected via spatial sampling. Then these selected features are finally concatenated together into a single feature super-vector using serial fusion. This concatenated feature vector is used to perform classification. Experimental results from face and iris standard biometric databases are presented. The reported results clearly show the performance improvements in classification obtained by applying feature level fusion for both multi-modal and multi-unit biometrics in comparison to uni-modal classification and score level fusion

Feature Level Fusion of Face and Fingerprint Biometrics

The aim of this paper is to study the fusion at feature extraction level for face and fingerprint biometrics. The proposed approach is based on the fusion of the two traits by extracting independent feature pointsets from the two modalities, and making the two pointsets compatible for concatenation. Moreover, to handle the problem of curse of dimensionality, the feature pointsets are properly reduced in dimension. Different feature reduction techniques are implemented, prior and after the feature pointsets fusion, and the results are duly recorded. The fused feature pointset for the database and the query face and fingerprint images are matched using techniques based on either the point pattern matching, or the Delaunay triangulation. Comparative experiments are conducted on chimeric and real databases, to assess the actual advantage of the fusion performed at the feature extraction level, in comparison to the matching score level.Comment: 6 pages, 7 figures, conferenc

Responsabilita ed irresponsabilita sociale d'impresa in materia di diritti umani nei paesi avanzati

La presente tesi si pone come obiettivo quello di analizzare i comportamenti responsabili ed irresponsabili da parte delle grandi imprese dei paesi avanzati. Nello specifico mira a capire se esiste una relazione tra l’adozione di politiche di Responsabilità sociale d’impresa (RSI) da parte delle aziende ed il loro coinvolgimento in abusi di diritti umani. Inoltre analizza la situazione rispetto ai paesi in cui le aziende pongono in essere i loro abusi

Visual Surveillance and Biometrics: Practices, Challenges, and Possibilities

Visual surveillance is the latest paradigm for social security through machine intelligence. It includes the use of visual data captured by infrared sensors or visible-light cameras mounted in cars, corridors, traffic signals etc. Visual surveillance facilitates the classification of human behavior, crowd activity, and gesture analysis to achieve application-specific objectivesinfo:eu-repo/semantics/publishedVersio

STUDIO PROCESSI AZIENDALI LEGATI AI SERVIZI PER STARTUP DI M31

Il candidato verrà integrato nella sede istituzionale di M31 a Monselice (PD), presso Villa Duodo, ove operano i manager della holding ed il personale amministrativo. Il candidato prenderà contatto con i responsabili delle principali funzioni dirigenziali di M31 al fine di comprendere prima e mappare poi i processi e servizi attraverso i quali M31 interagisce con le proprie partecipate. E' inoltre richiesta l'identificazione dei KPI che guidano l'attività quotidiana di M31, un'analisi di costing sui singoli processi individuati. Infine, per rendere più efficace il processo di Selezione degli Investimenti in M31, il candidato dovrà analizzare l'introduzione di una piattaforma per la gestione delle opportunità di investimento che giungono in input ad M31