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This article investigates the possibility to estimate the perceived Quality of Experience (QoE) automatically and unobtrusively by analyzing the face of the consumer of video streaming services, from which facial expression and gaze direction are extracted. If effective, this would be a valuable tool for the monitoring of personal QoE during video streaming services without asking the user to provide feedback, with great advantages for service management. Additionally, this would eliminate the bias of subjective tests and would avoid bothering the viewers with questions to collect opinions and feedback. The performed analysis relies on two different experiments: i) a crowdsourcing test, where the videos are subject to impairments caused by long initial delays and re-buffering events; ii) a laboratory test, where the videos are affected by blurring effects. The facial Action Units (AU) that represent the contractions of specific facial muscles together with the position of the eyes’ pupils are extracted to identify the correlation between perceived quality and facial expressions. An SVM with a quadratic kernel and a k-NN classifier have been tested to predict the QoE from these features. These have also been combined with measured application-level parameters to improve the quality prediction. From the performed experiments, it results that the best performance is obtained with the k-NN classifier by combining all the described features and after training it with both the datasets, with a prediction accuracy as high as 93.9% outperforming the state of the art achievements.

In this paper, we propose a novel IoT-based smart parking (SP) solution which has been designed to provide information on the status of parking spots offered in on-street parking areas. We mostly focused on the following issues of the state of the art solutions: scalability, interoperability to address the heterogeneity of IoT devices, low energy consumption, and timely prediction of the availability of the parking spots. To this we leverage the Social IoT (SIoT) Lysis environment to create virtual entities of the real world objects involved in the SP system for on-street parking areas. The usage of the social virtual entities allows for addressing the interoperability issues among different types of IoT devices used by separate solutions deployed in adjacent areas. Magnetometer sensors are used to automatically detect the presence of a vehicle in each parking spot and the sensors data are collected through concentrators that cover the whole parking areas through low-energy Wi-Fi. Additionally, a control dashboard has been designed and developed to manage the monitored parking areas and provide responsive data analytics regarding the occupancy of parking areas in the city, which can be accessed through an Android App. Finally, a smart payment service allows the users to automatically pay for the used services making use of Bluetooth beacons. Experiments have been performed with the developed test-bed to show the performance of the system to timely detect the presence of a vehicle and identify the owner ID to trigger the payment procedure.

Most Facial Expression Recognition (FER) systems rely on machine learning approaches that require large databases for an effective training. As these are not easily available, a good solution is to augment the databases with appropriate data augmentation (DA) techniques, which are typically based on either geometric transformation or oversampling augmentations (e.g., generative adversarial networks (GANs)). However, it is not always easy to understand which DA technique may be more convenient for FER systems because most state-of-the-art experiments use different settings which makes the impact of DA techniques not comparable. To advance in this respect, in this paper, we evaluate and compare the impact of using well-established DA techniques on the emotion recognition accuracy of a FER system based on the well-known VGG16 convolutional neural network (CNN). In particular, we consider both geometric transformations and GAN to increase the amount of training images. We performed cross-database evaluations: training with the "augmented" KDEF database and testing with two different databases (CK+ and ExpW). The best results were obtained combining horizontal reflection, translation and GAN, bringing an accuracy increase of approximately 30%. This outperforms alternative approaches, except for the one technique that could however rely on a quite bigger database.