Basketball Action Recognition (BAR) has received increasing attention in the fields of computer vision and artificial intelligence, serving as a fundamental component in performance evaluation, automated game annotation, tactical analysis, and referee decision-making support. Despite notable advancements driven by deep learning approaches, BAR remains a challenging task due to the inherent complexity of basketball movements, frequent occlusions, and limited availability of standardized benchmark datasets. This survey provides a comprehensive and structured synthesis of current developments in BAR research, encompassing four principal dimensions: dataset curation, computational methodologies, synthetic data generation, and model explainability. A critical analysis of publicly available basketball-specific datasets is presented, delineating their modalities, annotation strategies, action taxonomies, and representational scope. Furthermore, the survey offers a structured classification of state-of-the-art action recognition methodologies, ranging from video-based and skeleton-based models to sensor-driven and multimodal fusion approaches, emphasizing architectural characteristics, evaluation protocols, and task-specific adaptations. The role of synthetic data is systematically examined as a means to address data scarcity, reduce annotation noise, and enhance model generalization through controlled variability and simulation-based augmentation. In parallel, the integration of explainable artificial intelligence (XAI) techniques is also analyzed, with a focus on post-hoc attribution methods, probabilistic reasoning models, and interpretable neural architectures, aimed at improving the transparency and accountability of decision-making processes. The survey identifies persisting research challenges, including dataset heterogeneity, limitations in cross-domain transferability, and the accuracy-interpretability trade-off in deep models. By delineating current limitations and prospective directions, this work provides a foundational reference to guide the development of robust, generalizable, and explainable BAR systems for deployment in real-world sports intelligence applications. • A systematic review of Basketball Action Recognition Datasets. • Integration of Explainable AI in action recognition models for basketball sport. • Applications and limitations of action recognition in basketball sport. • The role of synthetic data in addressing dataset limitations of state-of-the-art.

Monocular vision-based 3D scene understanding has been an integral part of many machine vision applications. Always, the objective is to measure the depth using a single RGB camera, which is at par with the depth cameras. In this regard, monocular vision-guided autonomous navigation of robots is rapidly gaining popularity among the research community. We propose an effective monocular vision-assisted method to measure the depth of an Unmanned Aerial Vehicle (UAV) from an impending frontal obstacle. This is followed by collision-free navigation in unknown GPS-denied environments. Our approach deals upon the fundamental principle of perspective vision that the size of an object relative to its field of view (FoV) increases as the center of projection moves closer towards the object. Our contribution involves modeling the depth followed by its realization through scale-invariant SURF features. Noisy depth measurements arising due to external wind, or the turbulence in the UAV, are rectified by employing a constant velocity-based Kalman filter model. Necessary control commands are then designed based on the rectified depth value to avoid the obstacle before collision. Rigorous experiments with SURF scale-invariant features reveal an overall accuracy of 88.6% with varying obstacles, in both indoor and outdoor environments.

Language has always been one of humanity's defining characteristics. Visual Language Identification (VLI) is a relatively new field of research that is complex and largely understudied. In this paper, we present a preliminary study in which we use linguistic information as a soft biometric trait to enhance the performance of a visual (auditory-free) identification system based on lip movement. We report a significant improvement in the identification performance of the proposed visual system as a result of the integration of these data using a score-based fusion strategy. Methods of Deep and Machine Learning are considered and evaluated. To the experimentation purposes, the dataset called laBial Articulation for the proBlem of the spokEn Language rEcognition (BABELE), consisting of 8 different languages, has been created. It includes a collection of different features of which the spoken language represents the most relevant, while each sample is also manually labelled with gender and age of the subjects.

Emotion estimation from face expression analysis is nowadays a widely-explored computer vision task. In turn, the classification of expressions relies on relevant facial features and their dynamics. Despite the promising accuracy results achieved in controlled and favorable conditions, the processing of faces acquired at a distance, entailing low-quality images, still suffers from a significant performance decrease. In particular, most approaches and related computational models become extremely unstable in the case of the very small amount of useful pixels that is typical in these conditions. Therefore, their behavior should be investigated more carefully. On the other hand, real-time emotion recognition at a distance may play a critical role in smart video surveillance, especially when controlling particular kinds of events, e.g., political meetings, to try to prevent adverse actions. This work compares facial expression recognition at a distance by: 1) a deep learning architecture based on state-of-the-art (SOTA) proposals, which exploits the whole images to autonomously learn the relevant embeddings; 2) a machine learning approach that relies on hand-crafted features, namely the facial landmarks preliminarily extracted using the popular Mediapipe framework. Instead of using either the complete sequence of frames or only the final still image of the expression, like current SOTA approaches, the two proposed methods are designed to use rich temporal information to identify three different stages of emotion. Expressions are time-split accordingly into four phases to better exploit their temporal-dependent dynamics. Experiments were conducted on the popular Extended Cohn-Kanade dataset (CK+). It was chosen for its wide use in related literature, and because it includes videos of facial expressions and not only still images. The results show that the approach relying on machine learning via hand-crafted features is more suitable for classifying the initial phases of the expression and does not decay in terms of accuracy when images are at a distance (only 0.08% of decay). On the contrary, deep learning not only has difficulties classifying the initial phases of the expressions but also suffers from relevant performance decay when considering images at a distance (52.68% accuracy decay).

Facial Expression recognition is a computer vision problem that took relevant benefit from the research in deep learning. Recent deep neural networks achieved superior results, demonstrating the feasibility of recognizing the expression of a user from a single picture or a video recording the face dynamics. Research studies reveal that the most discriminating portions of the face surfaces that contribute to the recognition of facial expressions are located on the mouth and the eyes. The restrictions for COVID pandemic reasons have also revealed that state-of-the-art solutions for the analysis of the face can severely fail due to the occlusions of using the facial masks. This study explores to what extend expression recognition can deal with occluded faces in presence of masks. To a fairer comparison, the analysis is performed in different occluded scenarios to effectively assess if the facial masks can really imply a decrease in the recognition accuracy. The experiments performed on two public datasets show that some famous top deep classifiers expose a significant reduction in accuracy in presence of masks up to half of the accuracy achieved in non-occluded conditions. Moreover, a relevant decrease in performance is also reported also in the case of occluded eyes but the overall drop in performance is not as severe as in presence of the facial masks, thus confirming that, like happens for face biometric recognition, occluded faces by facial mask still represent a challenging limitation for computer vision solutions.

A facial expression recognition system that can provide quick assistance to the healthcare system and exceptional services to the patients is proposed in this article. The implementation of this work is divided into three components. In the first component, landmark points on the facial region are detected; a fixed-sized rectangular box is obtained by normalizing the detected face region, and then, down sampled to its varying sizes producing multiresolution images. Different convolution neural network architectures are proposed in the second component for analyzing the textual information within the multiresolution facial images. To extract more discriminating features and enhance the proposed system’s performance, some amalgamation of transfer learning, progressive image resizing, data augmentation, and fine tuning of parameters are employed in the third component. For experimental purposes, three benchmark databases, static facial expressions in the wild, Cohn-Kanade, and Karolinska directed emotional faces, are employed with some existing methods concerning these databases. The comparison with these databases shows the superiority of the proposed system.

Interconnectivity and smart automation of Internet of Things in recent times have led to the concept of Industry 4.0. Together with the improvement in productivity and new business models, employment conditions should take advantage of these new technologies. Safety in the workplace is one of the most sensitive topics on matters that needs targeted and accurate solutions. The safety can be guaranteed by investigating the attention states of the workers, and in particular, their drowsiness levels. Several technologies have faced this problem by using biometrics, but how many of them are applicable in a real-case-use scenario of Industry 4.0? This article aims to answer this question by discussing available data and methods that can be used in specific workplaces. We highlight their limitations and accuracy to sketch out the recent literature that may contribute to worker safety in Industry 4.0. Finally, we point out a gap that needs to be filled in order to implement these strategies on a large scale.

Pepper is a humanoid robot capable of expressing body language, perceiving, and interacting with its surrounding environment, thanks to a wide set of sensors and actuators and exposing capabilities and high-level interfaces for natural interaction with humans. In this article, we present the development of VPepper, the Pepper virtual replica, by describing experiences focused on the interaction of the digital twin with the replicas of the smart objects in a smart home. Pepper robot has been featured with arms and hands, but its motors and actuators cannot support intensive experimental sessions and training procedures to learn how safely touch objects. Here, digital twin metaphor plays a crucial role. By a virtual and reliable replica of the robot, machine learning procedures can be seamlessly moved to/from the digital twin with a significant speedup and preventing the physical robot from deterioration. As a practical application, the reported case study is inspired to ambient-assisted living in elderly assistance. The experience, as well as the entire design and development process, has revealed VPepper and the smart environment to offer interesting opportunities for the physical accuracy of the simulation and for the availability of machine learning instruments that may be converted and adopted for real settings. A final empirical evaluation, performed involving 25 volunteer caregivers, confirms the perceived value and the potential usefulness of the system.

Social robots adopt an emotional touch to interact with users inducing and transmitting humanlike emotions. Natural interaction with humans needs to be in real time and well grounded on the full availability of information on the environment. These robots base their way of communicating on direct interaction (touch, listening, view), supported by a range of sensors on the surrounding environment that provide a radially central and partial knowledge on it. Over the past few years, social robots have been demonstrated to implement different features, going from biometric applications to the fusion of machine learning environmental information collected on the edge. This article aims at describing the experiences performed and still ongoing and characterizes a simulation environment developed for the social robot Pepper that aims to foresee the new scenarios and benefits that tactile connectivity will enable.

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.

In the last fifteen years, smartphones have become very popular amongst the population, with the subsequent development of dozens of applications aimed at providing security to these portable devices. Nowadays, the cutting edge devices are also provided with biometric sensors (e.g., fingerprint sensors) allowing the users to access them without using the out-of-date alphanumerical password. In this work, we present a method that realizes iris recognition by means of Self Organizing Maps (SOM). In order to obtain a better refined and discriminative feature map, the RGB data of the iris, previously segmented, have been combined with two statistical descriptors. The algorithm has been designed specifically to require a low processing power, making it an ideal choice in the context of mobile devices.

The growing interest for mobile biometrics stems from the increasing need to secure personal data and services, which are often stored or accessed from there. Modern user mobile devices, with acquisition and computation resources to support related operations, are nowadays widely available. This makes this research topic very attracting and promising. Iris recognition plays a major role in this scenario. However, mobile biometrics still suffer from some hindering factors. The resolution of captured images and the computational power are not comparable to desktop systems yet. Furthermore, the acquisition setting is generally uncontrolled, with users who are not that expert to autonomously generate biometric samples of sufficient quality. Mobile Iris CHallenge Evaluation aims at providing a testbed to assess the progress of mobile iris recognition, and to evaluate the extent of its present limitations. This paper presents the results of the competition launched at the 2016 edition of the International Conference on Pattern Recognition (ICPR).

Mixed reality has been proposed for industrial applications such as AR-assisted manteinance and repair in which real equipment are augmented by visual aids. A possible complementary use of this technology for the same context can be achieved by performing virtual training in a “mixed reality environment” enabling to observe actual-size virtual replicas of real equipment within the physical space, providing an advantage in terms of perceptual impact and learning efficacy compared to traditional computer-based-training techniques. To this aim we present an effective hybrid tracking approach, exploiting both optical markers and inertial sensors to measure user's head position and rotation, providing both accuracy and robustness to fast head movements. A field-deployable tridimensionally-arranged marker-set, specifically designed for this kind of application, represents a further contribute of this paper, providing reliable close-to-medium distance optical tracking capabilities and increased robustness to challenging lighting conditions. First experiments confirm the potential of the proposed solution for this context and for many other applications as well.

In the last years, the growing diffusion of lightweight portable computing device like netbooks, tablets, and smartphones, featuring adequate processing power coupled with trackpad/touchpad interface, one or two webcams and eventually additional sensors (accelerometers, gps, gyroscopes, digital compass, etc.) has provided a low-cost platform to augmented reality applications, usually relying on more dedicated but also expensive and bulky technologies like motion tracking systems and see-through head mounted displays. In this paper we present and describe an AR application aimed to showcase how it is possible to effectively augment AV (Audio-Video) components, a kind of hi-tech gear today diffused in most home environments, by means of context dependent graphics contents. Visual aids in the form of both static and animated graphics are displayed according to the current status of the component (outputted via a serial interface) or simply based on the selection operated by the user through the trackpad. Moreover the system is able to help user to focus his/her attention on the physical interface on the AV component (e.g. a knob, a button or a connector in the back panel) either via an augmenting strategy (e.g. by adding virtual info) or by means of a diminishing approach (hiding all the other not relevant features). The proposal is easily extendible to a broad category of low-cost AR applications as it is based on cheap hardware (netbooks/tablets) and exploits marker based motion tracking through external webcam and the lcd screen as a see-through display.