Analysis of RGB-D camera technologies for supporting different facial usage scenarios

Recently a wide variety of applications has been developed integrating 3D functionalities. Advantages given by the possibility of relying on depth information allows the developers to design new algorithms and to improve the existing ones. In particular, for what concerns face morphology, 3D has led to the possibility to obtain face depth maps highly close to reality and consequently an improvement of the starting point for further analysis such as Face Detection, Face Authentication, Face Identification and Face Expression Recognition. The development of the aforementioned applications would have been impossible without the progress of sensor technologies for obtaining 3D information. Several solutions have been adopted over time. In this paper, emphasis is put on passive stereoscopy, structured light, time-of-flight (ToF) and active stereoscopy, namely the most used technologies for the cameras design and fulfilment according to the literature. The aim of this article is to investigate facial applications and to examine 3D camera technologies to suggest some guidelines for addressing the correct choice of a 3D sensor according to the application that has to be developed

Advances and perspectives on the ecology and management of Castanea species

Species of chestnut (Castanea spp.) are naturally widespread throughout temperate forests of the northern hemisphere in Asia, Europe, and North America. Populations have been naturalized outside of species’ native ranges in Europe, North America, South America and Oceania. The wide diffusion on a planetary level over tens of millions of years has resulted in high genetic variability within the genus and spe- cies adaptations to disparate environmental conditions (Dane et al., 2003; Mellano et al., 2012; Krebs et al., 2019). Perhaps more than many other tree species, the history of chestnut has been closely linked to human civilizations who utilized chestnut as an agricultural and forest resource over millennia. Chestnut species have had important cultural significance for Indigenous communities, although much Traditional Ecological Knowledge has been lost (Barnhill-Dilling and Delborne, 2019), and chestnut species have been subjected to challenges of the contemporary Anthropocene, from globalization to climate change. Al- terations to disturbance regimes, particularly related to drought and fire, and the introduction of nonnative pests and pathogens, have reduced genetic diversity and population densities, particularly for species in North America, Europe, and western Asia (Mellano et al., 2012; Dalgleish et al., 2016). Forest management practices, genomic tools, tree breeding, and prediction models have been developed and tested to meet these challenges (Jacobs et al., 2013; Fernandes et al., 2022). Most strategies, however, are underdeveloped and species spe- cific, including for American chestnut (Burnham et al., 1986; Ana- gnostakis, 2012; Fei et al., 2012) and sweet chestnut (Conedera et al., 2016; Manetti et al., 2019; Marcolin et al., 2020; Patrício et al., 2020). A global perspective for chestnut sustainability, conservation, and man- agement has largely been missing in the literature, excluding pro- ceedings from International Chestnut Symposia (e.g., Double and MacDonald, 2014).info:eu-repo/semantics/publishedVersio

A silvicultural synthesis of sweet (Castanea sativa) and American (C. dentata) chestnuts

Sweet chestnut (Castanea sativa) and American chestnut (C. dentata) have been explicitly linked to ancient, historical, and contemporary cultures while enhancing ecological services in forests in which they occur. Threats that currently face these chestnut species are unprecedented and additive, including global climate change, nonnative pest and pathogen species, land use changes, and lack of scientific knowledge and technologies. In this paper, we provide a synthesis of traditional and novel silvicultural systems for chestnut, focusing on these two important species. We frame the discussion within the context of the species’ cultural and ecological significances, scientific knowledge bases, and associated knowledge gaps. Sweet and American chestnuts require divergent strategies to sustain their conservation values due to differing cultural and ecological landscapes and biological stressors. Both species share the need to conduct active forest management to maintain or restore populations in native or naturalized habitats. Even-aged management is the preferred regeneration method for both species. Coppicing that is commonly implemented for sweet chestnut can provide a potential strategy for American chestnut once disease-resistant material becomes widely available. Blight caused by Cryphonectria parasitica may limit long rotation timber production of American chestnut, even for resistant material, making short-rotation systems a more attractive management option. Advanced artificial regeneration and breeding strategies have been developed for American chestnut but are largely underdeveloped for sweet chestnut. High forests of sweet chestnut can play an important role in new single or mixed species plantations, naturalized stands, or in naturally regenerated stands for production of medium-large dimension timber. American chestnut will likely be managed as a minor to moderate component of mixed species forests to achieve ecological restoration goals. A close-to-nature silvicultural approach has not been tested for either species and may be difficult to implement due to the threats from changing climate conditions and nonnative pathogens. Traditional and emerging markets of sweet chestnut, such as biomass or carbon markets, may help inform future opportunities around American chestnut for tribal and rural communities. Climate change and other threats call for synergistic partnerships and knowledge sharing to maintain or restore sweet and American chestnuts as part of the global ecosystem.This research was in part funded by: Chilean Ministry of Agriculture (Development and contributions for the use of forest and fruit species of high value for Chile, INFOR); ANID BASAL FB210015 (CENAMAD); the United States Department of Agriculture Forest Service; the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC), CIMO (UIDB/00690/2020 and UIDP/00690/2020); and SusTEC (LA/P/0007/2020).info:eu-repo/semantics/publishedVersio

Effects of 6 weeks of traditional resistance training or high intensity interval resistance training on body composition, aerobic power and strength in healthy young subjects: A randomized parallel trial

Consistent practice of physical activity has well known positive effects on general health; however, time for exercise remains one major barrier for many. An acute bout of high intensity interval resistance training (HIIRT) increases acute resting energy expenditure (REE) and decreases respiratory ratio (RR), suggesting its potential role on weight loss and increased fatty acid oxidation. The aim of this study was to test the long-term effect of HIIRT on body composition, lipid profile and muscle strength using a randomized parallel trial. Twenty healthy young adults (22.15 ± 1.95 years) were randomized to perform either a HIIRT (N = 11) protocol, consisting of three sets of 6 repetitions at 6 repetition maximum (RM) and then 20 seconds of rest between repetitions until exhaustion repeated for 3 times with 2’30″ rest between sets or a traditional training (TRT, N = 9) protocol of 3 sets of 15 reps with 75 sec of rest between sets. Body composition, resting energy metabolism, aerobic capacity, muscle strength and blood measurements were taken before and after 8 weeks of training. Both protocols enhanced muscle strength, but only HIIRT improved endurance strength performance (+22.07%, p < 0.05) and lean body mass (+2.82%, p < 0.05). REE and RR were unaltered as lipid profile. HIIRT represents a valid training method to improve muscle strength and mass, but its role on body weight control was not confirmed

Can adas distract driver’s attention? An rgb-d camera and deep learning-based analysis

Driver inattention is the primary cause of vehicle accidents; hence, manufacturers have introduced systems to support the driver and improve safety; nonetheless, advanced driver assistance systems (ADAS) must be properly designed not to become a potential source of distraction for the driver due to the provided feedback. In the present study, an experiment involving auditory and haptic ADAS has been conducted involving 11 participants, whose attention has been monitored during their driving experience. An RGB-D camera has been used to acquire the drivers’ face data. Subsequently, these images have been analyzed using a deep learning-based approach, i.e., a convolutional neural network (CNN) specifically trained to perform facial expression recognition (FER). Analyses to assess possible relationships between these results and both ADAS activations and event occurrences, i.e., accidents, have been carried out. A correlation between attention and accidents emerged, whilst facial expressions and ADAS activations resulted to be not correlated, thus no evidence that the designed ADAS are a possible source of distraction has been found. In addition to the experimental results, the proposed approach has proved to be an effective tool to monitor the driver through the usage of non-invasive techniques

Building an ecologically valid facial expression database – Behind the scenes

Artificial Intelligence (AI) algorithms, together with a general increased computational performance, allow nowadays exploring the use of Facial Expression Recognition (FER) as a method of recognizing human emotion through the use of neural networks. The interest in facial emotion and expression recognition in real-life situations is one of the current cutting-edge research challenges. In this context, the creation of an ecologically valid facial expression database is crucial. To this aim, a controlled experiment has been designed, in which thirty-five subjects aged 18–35 were asked to react spontaneously to a set of 48 validated images from two affective databases, IAPS and GAPED. According to the Self-Assessment Manikin, participants were asked to rate images on a 9-points visual scale on valence and arousal. Furthermore, they were asked to select one of the six Ekman’s basic emotions. During the experiment, an RGB-D camera was also used to record spontaneous facial expressions aroused in participants storing both the color and the depth frames to feed a Convolutional Neural Network (CNN) to perform FER. In every case, the prevalent emotion pointed out in the questionnaires matched with the expected emotion. CNN obtained a recognition rate of 75.02%, computed comparing the neural network results with the evaluations given by a human observer. These preliminary results have confirmed that this experimental setting is an effective starting point for building an ecologically valid database

Antenatal automatic diagnosis of cleft lip via unsupervised clustering method relying on 3D facial soft tissue landmarks

Objectives Ultrasound (US) is the first-choice device to detect different types of facial dysmorphisms. Anyway, at present no standard protocol has been defined for automatic nor semi-automatic diagnosis. Even though the practitioner's contribution is core, steps towards automatism are to be undertaken. We propose a methodology for diagnosing cleft lip on 3D US scans. Methods A bounded Depth Minimum Steiner Trees (D-MST) clustering algorithm is proposed for discriminating groups of 3D US faces relying on the presence/absence of a cleft lip. The analysis of 3D facial surfaces via Differential Geometry is adopted to extract landmarks. Thus, the extracted geometrical information is elaborated to feed the unsupervised clustering algorithm and produce the classification. The clustering returns the probability of being affected by the pathology, allowing physicians to focus their attention on risky individuals for further analysis. Results The feasibility is tested upon the available 3D US scans data and then deeply investigated for a large dataset of adult individuals. 3D facial Bosphorus database is chosen for the testing, which seven cleft lip-affected individuals are added to, by artificially creating the defect. The algorithm correctly separates left and right-sided cleft lips, while healthy individuals create a unique cluster; thus, the method shows accurate diagnosis results. Conclusions Even if further testing is to be performed on tailored datasets made exclusively of fetal images, this techniques gives hefty hints for a future tailored algorithm. This method also fosters the investigation of the scientific formalisation of the "normotype", which is the representative face of a class of individuals, collecting all the principal anthropometric facial measurements, in order to recognise a normal or syndromic fetus