Soft biometric systems have spread among recent years, both for powering classical biometrics, as well as stand alone solutions with several application scopes ranging from digital signage to human-robot interaction. Among all, in the recent years emerged the possibility to consider as a soft biometrics also the temporal evolution of the human gaze and some recent works in the literature explored this exciting research line by using expensive and (perhaps) unsafe devices which require user cooperation to be calibrated. This work is instead the first attempt to perform biometric identification of individuals on the basis of data acquired by a low-cost, non-invasive, safe and calibration-free gaze estimation framework consisting of two main components conveniently combined and performing user's head pose estimation and eyes' pupil localization on data acquired by a RGB-D device. The experimental evidence of the feasibility of using the proposed framework as soft-biometrics is given on a set of users watching three benchmark heterogeneous videos in an unconstrained environment.

Gender recognition is a topic of high interest especially in the growing field of audience measurement techniques for digital signage applications. Usually, supervised approaches are employed and they require a preliminary training phase performed on large datasets of annotated facial images that are expensive (e.g. MORPH) and, anyhow, they cannot be updated to keep track of the continuous mutation of persons' appearance due to changes of fashions and styles (e.g. hairstyles or makeup). The use of small-sized (and then updatable in a easier way) datasets is thus high desirable but, unfortunately, when few examples are used for training, the gender recognition performances dramatically decrease since the state-of-art classifiers are unable to handle, in a reliable way, the inherent data uncertainty by explicitly modeling encountered distortions. To face this drawback, in this work an innovative classification scheme for gender recognition has been introduced: its core is the Minimax approach, i.e. a smart classification framework that, including a number of existing regularized regression models, allows a robust classification even when few examples are used for training. This has been experimentally proved by comparing the proposed classification scheme with state of the art classifiers (SVM, kNN and Random Forests) under various pre-processing methods.

This paper presents a detailed study about different algorithmic configurations for estimating soft biometric traits. In particular, a recently introduced common framework is the starting point of the study: it includes an initial facial detection, the subsequent facial traits description, the data reduction step, and the final classification step. The algorithmic configurations are featured by different descriptors and different strategies to build the training dataset and to scale the data in input to the classifier. Experimental proofs have been carried out on both publicly available datasets and image sequences specifically acquired in order to evaluate the performance even under real-world conditions, i.e., in the presence of scaling and rotation.

Unmanned aerial vehicles (UAVs) are an active research field since several years. They can be applied in a large variety of different scenarios, and supply a test bed to investigate several unsolved problems such as path planning, control and navigation. Furthermore, with the availability of low cost, robust and small video cameras, UAV video has been one of the fastest growing data sources in the last couple of years. In other words, object detection and tracking as well as visual navigation has recently received a lot of attention. This paper proposes an advanced technology framework that, through the use of UAVs, allows to supervise a specific sensible area (i.e. traffic monitoring, dangerous zone and so on). In particular, one of the most cited real-rime visual tracker proposed in the literature, Struck, is applied on video sequences tipically supplied by UAVs equipped with amonocular camera. Furthermore in this paper is investigated on the feasibility to graft different features characterization into the original tracking architecture (replacing the orginal ones). The used feature extraction methods are based on Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG). Objects to be tracked could be selected manually or by means of advanced detection technique based, for example, on change detection or template matching strategies. The experimental results on well known benchmark sequences show as these features replacing improve the overall performances of the original considered real-time visual tracker.

Automatic Facial Expression Recognition is a topic of high interest especially due to the growing diffusion of assistive computing applications, as Human Robot Interaction, where a robust awareness of the people emotion is a key point. This paper proposes a novel automatic pipeline for facial expression recognition based on the analysis of the gradients distribution, on a single image, in order to characterize the face deformation in different expressions. Firstly, an accurate investigation of optimal HOG parameters has been done. Successively, a wide experimental session has been performed demonstrating the higher detection rate with respect to other State-of-the-Art methods. Moreover, an on-line testing session has been added in order to prove the robustness of our approach in real environments.

A variety of optical investigation methods applied to paintings are, by now, an integral part of the repair process, both to plan the restoration intervention and to monitor its various phases. Among them infrared reflectography in wide-band modality is traditionally employed in non-invasive diagnostics of ancient paintings to reveal features underlying the pictorial layer thanks to transparency characteristics to NIR radiation of most of the materials composing the paints. This technique was improved with the introduction of the multi-spectral modality that consists in acquiring the radiation back scattered from the painting into narrow spectral bands. The technology, widely used in remote sensing applications such as satellite or radar imaging, has only recently gained importance in the field of artwork conservation thanks to the varied reflectance and transmittance of pigments over this spectral region. In this work we present a scanning device for multi-NIR spectral imaging of paintings, based on contact-less and single-point measurement of the reflectance of painted surfaces. The back-scattered radiation is focused on square-shaped fiber bundle that carries the light to an array of 16 photodiodes equipped with pass-band filters so to cover the NIR spectral range from 900 to 2500 nm. In particular, we describe the last instrument upgrade that consists in the addition of an autofocus system that keeps the optical head perfectly focused during the scanning. The output of the autofocus system can be used as a raw map of the painting shape.

Automatic facial expression recognition (FER) is a topic of growing interest mainly due to the rapid spread of assistive technology applications, as human-robot interaction, where a robust emotional awareness is a key point to best accomplish the assistive task. This paper proposes a comprehensive study on the application of histogram of oriented gradients (HOG) descriptor in the FER problem, highlighting as this powerful technique could be effectively exploited for this purpose. In particular, this paper highlights that a proper set of the HOG parameters can make this descriptor one of the most suitable to characterize facial expression peculiarities. A large experimental session, that can be divided into three different phases, was carried out exploiting a consolidated algorithmic pipeline. The first experimental phase was aimed at proving the suitability of the HOG descriptor to characterize facial expression traits and, to do this, a successful comparison with most commonly used FER frameworks was carried out. In the second experimental phase, different publicly available facial datasets were used to test the system on images acquired in different conditions (e.g. image resolution, lighting conditions, etc.). As a final phase, a test on continuous data streams was carried out on-line in order to validate the system in real-world operating conditions that simulated a real-time human-machine interaction.

Estimation of demographic information from video sequence with people is a topic of growing interest in the last years. Indeed automatic estimation of audience statistics in digital signage as well as the human interaction in social robotic environment needs of increasingly robust algorithm for gender, race and age classification. In the present paper some of the state of the art features descriptors and sub space reduction approaches for gender, race and age group classification in video/image input are analyzed. Moreover a wide discussion about the influence of dataset distribution, balancing and cardinality is shown. The aim of our work is to investigate the best solution for each classification problem both in terms of estimation approach and dataset training. Additionally the computational problem it considered and discussed in order to contextualize the topic in a practical environment.

Automatic facial expression recognition is one of the most interesting problem as it impacts on important applications in human-computer interaction area. Many applications in this field require real-time performance but not all the approach are suitable to satisfy this requirement. Geometrical features are usually the most light in terms of computational load but sometimes they exploits a huge number of features and do not cover all the possible geometrical aspect. In order to face up this problem, we propose an automatic pipeline for facial expression recognition that exploits a new set of 32 geometric facial features from a single face side covering a wide set of geometrical peculiarities. As a results, the proposed approach showed a facial expression recognition accuracy of 95,46% with a six-class expression set and an accuracy of 94,24% with a seven-class expression set.

This work introduces a real-time system able to lead humanoid robot behavior depending on the gender of the interacting person. It exploits Aldebaran NAO humanoid robot view capabilities by applying a gender prediction algorithm based on the face analysis. The system can also manage multiple persons at the same time, recognizing if the group is composed by men, women or is a mixed one and, in the latter case, to know the exact number of males and females, customizing its response in each case. The system can allow for applications of human-robot interaction requiring an high level of realism, like rehabilitation or artificial intelligence.

In thiswork, a real-time system able to automatically recognize soft-biometric traits is introduced and used to improve the capability of a humanoid robot to interact with humans. In particular the proposed system is able to estimate gender and age of humans in images acquired from the embedded camera of the robot. This knowledge allows the robot to properly react with customized behaviors related to the gender/age of the interacting individuals. The system is able to handle multiple persons in the same acquired image, recognizing the age and gender of each person in the robot's field of view. These features make the robot particularly suitable to be used in socially assistive applications.

This work introduces biometrics as a way to improve human-robot interaction. In particular, gender and age estimation algorithms are used to provide awareness of the user biometrics to a humanoid robot (Aldebaran NAO), in order to properly react with a specific gender/age behavior. The system can also manage multiple persons at the same time, recognizing the age and gender of each participant. All the estimation algorithms employed have been validated through a k-fold test and successively practically tested in a real human-robot interaction environment, allowing for a better natural interaction. Our system is able to work at a frame rate of 13 fps with 640×480 images taken from NAO's embedded camera. The proposed application is well-suited for all assisted environments that consider the presence of a socially assistive robot like therapy with disable people, dementia, post-stroke rehabilitation, Alzheimer disease or autism.

This paper proposes an advanced technology framework that, through the use of UAVs, allows to monitor archaeological sites. In particular this paper focuses on the development of computer vision techniques such as super-resolution and mosaicking aiming at extracting detailed and panoramic views of the sites. In particular, super-resolution aims at providing imagery solutions (from aerial and remote sensing platforms) that create higher resolution views that make visible some details that are not perceivable in the acquired images. Mosaicking aims, instead, at creating a unique large still image from the sequence of video frames contained in a motion imagery clip. In this way large areas can be observed and a global analysis of their temporal changes can be performed. In general super-resolution and mosaicking can be exploited both for touristic or surveillance purposes. In particular they can be efficiently used to allow the enjoyment of the cultural heritage through a fascinating visual experience also eventually containing augmented information but also for surveillance tasks that can help to detect or prevent illegal activities.