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In recent years, there has been a notable surge in the significance attributed to technologies facilitating secure and efficient cohabitation and collaboration between humans and machines, with a particular interest in robotic systems. A pivotal element in actualizing this novel and challenging collaborative paradigm involves different technical tasks, including the comprehension of 3D poses exhibited by both humans and robots through the utilization of non-intrusive systems, such as cameras. In this scenario, the availability of vision-based systems capable of detecting in real-time the robot’s pose is needed as a first step towards a safe and effective interaction to, for instance, avoid collisions. Therefore, in this work, we propose a vision-based system, referred to as D-SPDH, able to estimate the 3D robot pose. The system is based on double-branch architecture and depth data as a single input; any additional information regarding the state of the internal encoders of the robot is not required. The working scenario is the Sim2Real, i.e., the system is trained only with synthetic data and then tested on real sequences, thus eliminating the time-consuming acquisition and annotation procedures of real data, common phases in deep learning algorithms. Moreover, we introduce SimBa++, a dataset featuring both synthetic and real sequences with new real-world double-arm movements, and that represents a challenging setting in which the proposed approach is tested. Experimental results show that our D-SPDH method achieves state-of-the-art and real-time performance, paving the way a possible future non-invasive systems to monitor human-robot interactions.

Morphing Attack, i.e. the elusion of face verification systems through a facial morphing operation between a criminal and an accomplice, has recently emerged as a serious security threat. Despite the importance of this kind of attack, the development and comparison of Morphing Attack Detection (MAD) methods is still a challenging task, especially with deep learning approaches. Specifically, the lack of public datasets, the absence of common training and validation protocols, and the limited release of public source code hamper the reproducibility and objective comparison of new MAD systems. Usually, these aspects are mainly due to privacy concerns, that limit data transfers and storage, and to the recent introduction of the MAD task. Therefore, in this paper, we propose and publicly release Revelio, a modular framework for the reproducible development and evaluation of MAD systems. We include an overview of the modules, and describe the plugin system providing the possibility of extending native components with new functionalities. An extensive cross-datasets experimental evaluation is conducted to validate the framework and the performance of trained models on several publicly-released datasets, and to deeply analyze the main challenges in the MAD task based on single input images. We also propose a new metric, namely WAED, to summarize in a single value the error-based metrics commonly used in the MAD task, computed over different datasets, thus facilitating the comparative evaluation of different approaches. Finally, by exploiting Revelio, a new state-of-the-art MAD model (on SOTAMD single-image benchmark) is proposed and released.

For decades, researchers of different areas, ranging from artificial intelligence to computer vision, have intensively investigated human-centered data, i [...]

Morphing attack is an important security threat for automatic face recognition systems. High-quality morphed images, i.e. images without significant visual artifacts such as ghosts, noise, and blurring, exhibit higher chances of success, being able to fool both human examiners and commercial face verification algorithms. Therefore, the availability of large sets of high-quality morphs is fundamental for training and testing robust morphing attack detection algorithms. However, producing a high-quality morphed image is an expensive and time-consuming task since manual post-processing is generally required to remove the typical artifacts generated by landmark-based morphing techniques. This work describes an approach based on the Conditional Generative Adversarial Network paradigm for automated morphing artifact retouching and the use of Attention Maps to guide the generation process and limit the retouch to specific areas. In order to work with high-resolution images, the framework is applied on different facial crops, which, once processed and retouched, are accurately blended to reconstruct the whole morphed face. Specifically, we focus on four different squared face regions, i.e. the right and left eyes, the nose, and the mouth, that are frequently affected by artifacts. Several qualitative and quantitative experimental evaluations have been conducted to confirm the effectiveness of the proposal in terms of, among the others, pixel-wise metrics, identity preservation, and human observer analysis. Results confirm the feasibility and the accuracy of the proposed framework.

The accuracy and the overall performances of ophthalmic instrumentation, where specific analysis of eye images is involved, can be negatively influenced by invalid or incorrect frames acquired during everyday measurements of unaware or non-collaborative human patients and non-technical operators. Therefore, in this paper, we investigate and compare the adoption of several vision-based classification algorithms belonging to different fields, i.e., Machine Learning, Deep Learning, and Expert Systems, in order to improve the performance of an ophthalmic instrument designed for the Pupillary Light Reflex measurement. To test the implemented solutions, we collected and publicly released PopEYE as one of the first datasets consisting of 15 k eye images belonging to 22 different subjects acquired through the aforementioned specialized ophthalmic device. Finally, we discuss the experimental results in terms of classification accuracy of the eye status, as well as computational load analysis, since the proposed solution is designed to be implemented in embedded boards, which have limited hardware resources in computational power and memory size.

Face morphing and related morphing attacks have emerged as a serious security threat for automatic face recognition systems and a challenging research field. Therefore, the availability of effective and reliable morphing attack detectors is strongly needed. In this paper, we proposed a framework based on a double Siamese architecture to tackle the morphing attack detection task in the differential scenario, in which two images, a trusted live acquired image and a probe image (morphed or bona fide) are given as the input for the system. In particular, the presented framework aimed to merge the information computed by two different modules to predict the final score. The first one was designed to extract information about the identity of the input faces, while the second module was focused on the detection of artifacts related to the morphing process. Experimental results were obtained through several and rigorous cross-dataset tests, exploiting three well-known datasets, namely PMDB, MorphDB, and AMSL, containing automatic and manually refined facial morphed images, showing that the proposed framework was able to achieve satisfying results.

Nowadays, we are witnessing the wide diffusion of active depth sensors. However, the generalization capabilities and performance of the deep face recognition approaches that are based on depth data are hindered by the different sensor technologies and the currently available depth-based datasets, which are limited in size and acquired through the same device. In this paper, we present an analysis on the use of depth maps, as obtained by active depth sensors and deep neural architectures for the face recognition task. We compare different depth data representations (depth and normal images, voxels, point clouds), deep models (two-dimensional and three-dimensional Convolutional Neural Networks, PointNet-based networks), and pre-processing and normalization techniques in order to determine the configuration that maximizes the recognition accuracy and is capable of generalizing better on unseen data and novel acquisition settings. Extensive intra- and cross-dataset experiments, which were performed on four public databases, suggest that representations and methods that are based on normal images and point clouds perform and generalize better than other 2D and 3D alternatives. Moreover, we propose a novel challenging dataset, namely MultiSFace, in order to specifically analyze the influence of the depth map quality and the acquisition distance on the face recognition accuracy.

The recent spread of low-cost and high-quality RGB-D and infrared sensors has supported the development of Natural User Interfaces (NUIs) in which the interaction is carried without the use of physical devices such as keyboards and mouse. In this paper, we propose a NUI based on dynamic hand gestures, acquired with RGB, depth and infrared sensors. The system is developed for the challenging automotive context, aiming at reducing the driver’s distraction during the driving activity. Specifically, the proposed framework is based on a multimodal combination of Convolutional Neural Networks whose input is represented by depth and infrared images, achieving a good level of light invariance, a key element in vision-based in-car systems. We test our system on a recent multimodal dataset collected in a realistic automotive setting, placing the sensors in an innovative point of view, i.e., in the tunnel console looking upwards. The dataset consists of a great amount of labelled frames containing 12 dynamic gestures performed by multiple subjects, making it suitable for deep learning-based approaches. In addition, we test the system on a different well-known public dataset, created for the interaction between the driver and the car. Experimental results on both datasets reveal the efficacy and the real-time performance of the proposed method.

Face verification is the task of checking if two provided images contain the face of the same person or not. In this work, we propose a fully-convolutional Siamese architecture to tackle this task, achieving state-of-the-art results on three publicly-released datasets, namely Pandora, High-Resolution Range-based Face Database (HRRFaceD), and CurtinFaces. The proposed method takes depth maps as the input, since depth cameras have been proven to be more reliable in different illumination conditions. Thus, the system is able to work even in the case of the total or partial absence of external light sources, which is a key feature for automotive applications. From the algorithmic point of view, we propose a fully-convolutional architecture with a limited number of parameters, capable of dealing with the small amount of depth data available for training and able to run in real time even on a CPU and embedded boards. The experimental results show acceptable accuracy to allow exploitation in real-world applications with in-board cameras. Finally, exploiting the presence of faces occluded by various head garments and extreme head poses available in the Pandora dataset, we successfully test the proposed system also during strong visual occlusions. The excellent results obtained confirm the efficacy of the proposed method.