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

Malar augmentation with zygomatic osteotomy in orthognatic surgery: Bone and soft tissue changes threedimensional evaluation: Malar Augmentation in Orthognatic Surgery

Background: The aim of this prospective study is to objectively assess 3D soft tissue and bone changes of the malar region by using the malar valgization osteotomy in concomitant association with orthognatic surgery. Materials and methods: From January 2015 to January 2018, 10 patients who underwent single stage bilateral malar valgization osteotomy in conjunction with maxillo-mandibular orthognatic procedures for aesthetic and functional correction were evaluated. Clinical and surgical reports were collected and patient satisfaction was evaluated with a VAS score. For each patient, maxillofacial CT-scans were collected 1 month preoperatively (T0) and 6 months after the operation (T1). DICOM data were imported and elaborated in the software MatLab, which creates a 3D soft tissue model of the face. 3D Bone changes were assessed importing DICOM data into iPlan (BrainLAB 3.0) software and the superimposition process was achieved using autofusion. Descriptive statistical analyses were obtained for soft tissue and bone changes. Results: Considering bone assessment the comparison by superimposition between T0 and T1 showed an increase of the distance between bilateral malar prominence (Pr – Pl) and a slight forward movement (87,65 ± 1,55 to 97,60 ± 5,91); p-value 0.007. All of the patients had improvement of α angle, ranging from 36,30 ± 1,70 to 38,45 ± 0,55, p-value 0,04 (αr) and 36,75 ± 1,58 to 38,45 ± 0,35; p-value 0,04 (αl). The distance S increased from 78,05 ± 2,48 to 84,2 ± 1,20; p-value 0,04 (Sr) and 78,65 ± 2,16 to 82,60 ± 0,90 (Sl); p-value 0,03. Considering the soft tissue, the comparison by superimposition between T0 and T1 showed an antero-lateral movement (p-value 0.008 NVL; p-value 0.001 NVR) of the malar bone projection together with an increase in width measurements (p-value 0,05 VL; p-value 0,01 VR). Angular measurement confirmed the pattern of the bony changes (p-value 0.034 αL; p-value 0,05 αR). Conclusion: The malar valgization osteotomy in conjunction with orthognatic surgery is effective in improving zygomatic projection contributing to a balanced facial correction in midface hypoplasia.3D geometrical based volume and surface analysis demonstrate an increase in transversal and forward direction. The osteotomy can be safely performed in conjunction with orthognatic procedures

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

Methane emissions from a flooded rice field in the south of Brazil.

The State of Rio Grande do Sul in Brazil cultivates about 1Mha of rice in paddy fields. The soils are prepared using either conventional tillage (CT, 41% of the area) or no tillage (NT, 14% of area), the remaining falling in a mixed soil-preparation category. The outcomes of the current study represent the first evaluation of CH4 emissions from flooded rice fields in the south of Brazil. This information will feed the Brazilian greenhouse gas inventory. The study was carried out from January through March 2003 at the IRGA experimental station located in the municipality of Cachoerinha, Rio Grande do Sul, Brazil. Rice has been cultivated in this Gleisol area since 1994 using either the CT or NT system. The closed chamber method was used to collect air samples from 9:00 AM to 12:00 Noon on a weekly basis or in 24-hour campaigns; samples were analyzed using gas chromatography. Soil and plant parameters were also measured in order to determine which ecosystem factors affect CH4 emissions from the soil into the atmosphere. Along the period, CH4 emission rates varied from 24 to 703 mg m-2 day-1. NT plot emissions were initially greater than those from the CT plot, probably due to having maintained the crop residues on the surface of soil in the NT system. Nevertheless, CH4 emission rates in the CT plot were higher than in the NT plot 14 days after flooding, probably due to the higher root mass in the deeper soil layer in the NT system. The close relationship (P<0.01) found between CH4 emissions and soil temperature in both systems explains 60% of CH4 emissions. Total CH4 emissions were 33 and 22 g m-2 in the CT and NT systems, respectively. The emission variation between the soil preparation systems corresponds to 2,860 kg ha-1 CO2 equivalents. Moreover, this reduction represents 0.8 Mg ha-1 yr-1 C equivalents, greater than the average value of 0.58 Mg C ha-1 year-1 for C sequestration in agricultural soils in the subtropical region of Brazil. The 24-hour campaign emissions produced a sigmoid curve into both the atmosphere and the chamber, albeit with an inverse relationship. The 24-hour emissions were controlled by the soil and flood-water temperatures

A simple cytofluorimetric score may optimize testing for biallelic CEBPA mutations in patients with acute myeloid leukemia

Acute myeloid leukemia with biallelic mutation of CEBPA (CEBPA-dm AML) is a distinct good prognosis entity recognized by WHO 2016 classification. However, testing for CEBPA mutation is challenging, due to the intrinsic characteristics of the mutation itself. Indeed, molecular analysis cannot be performed with NGS technique and requires Sanger sequencing. The association of recurrent mutations or translocations with specific immunophenotypic patterns has been already reported in other AML subtypes. The aim of this study was the development of a specific cytofluorimetric score (CEBPA-dm score), in order to distinguish patients who are unlikely to harbor the mutation. To this end, the correlation of CEBPA-dm score with the presence of the mutation was analyzed in 50 consecutive AML patients with normal karyotype and without NPM1 mutation (that is mutually exclusive with CEBPA mutation). One point each was assigned for expression of HLA DR, CD7, CD13, CD15, CD33, CD34 and one point for lack of expression of CD14. OS was not influenced by sex, age and CEBPA-dm score. Multivariate OS analysis showed that CEBPA-dm (p &lt; 0.02) and FLT3-ITD (p &lt; 0.01) were the strongest independent predictors of OS. With a high negative predictive value (100%), CEBPA-dm score &lt; 6 was able to identify patients who are unlikely to have the mutation. Therefore, the application of this simple score might optimize the use of expensive and time-consuming diagnostic and prognostic assessment in the baseline work up of AML patients