Local feature extraction based facial emotion recognition: a survey

Notwithstanding the recent technological advancement, the identification of facial and emotional expressions is still one of the greatest challenges scientists have ever faced. Generally, the human face is identified as a composition made up of textures arranged in micro-patterns. Currently, there has been a tremendous increase in the use of local binary pattern based texture algorithms which have invariably been identified to being essential in the completion of a variety of tasks and in the extraction of essential attributes from an image. Over the years, lots of LBP variants have been literally reviewed. However, what is left is a thorough and comprehensive analysis of their independent performance. This research work aims at filling this gap by performing a large-scale performance evaluation of 46 recent state-of-the-art LBP variants for facial expression recognition. Extensive experimental results on the well-known challenging and benchmark KDEF, JAFFE, CK and MUG databases taken under different facial expression conditions, indicate that a number of evaluated state-of-the-art LBP-like methods achieve promising results, which are better or competitive than several recent state-of-the-art facial recognition systems. Recognition rates of 100%, 98.57%, 95.92% and 100% have been reached for CK, JAFFE, KDEF and MUG databases, respectively

Mechanical and energy absorption properties of 3D-printed honeycomb structures with Voronoi tessellations

3D printing technology is the new frontier in building construction. It is especially useful for making small structures within a short period. Full construction, including interior partitions and exterior façades, can be achieved with this technology. This paper proposes a parametric Voronoi tessellations model for quickly generating and fabricating 3D-printed hexagonal honeycomb partitions for interior design. Comprehensive experimental testing was conducted to characterize the mechanical properties and investigate the energy absorption characteristics of the proposed 3D-printed hexagonal honeycomb while comparing it to alternative hexagonal honeycomb structures. The tests included tensile testing (ASTM-D638) of the printed Polylactic Acid (PLA) material, especially with the almost total absence of conducted research that reported mechanical properties for 3D printed material with low infill percentages such as 10%. In addition, an in-plane quasi-static axial compression testing of the lightweight honeycomb structures was also conducted on the printed structure with the same low infill percentage. Compared to non-Voronoi honeycomb structures, the Voronoi honeycomb resulted in superior mechanical and energy absorption properties with energy absorption values ranging from 350 to 435 J and crash force efficiency being 1.42 to 1.65

Impact of severity, duration, and etiology of hyperthyroidism on bone turnover markers and bone mineral density in men

<p>Abstract</p> <p>Background</p> <p>Hyperthyroidism is accompanied by osteoporosis with higher incidence of fracture rates. The present work aimed to study bone status in hyperthyroidism and to elucidate the impact of severity, duration, and etiology of hyperthyroidism on biochemical markers of bone turnover and bone mineral density (BMD).</p> <p>Methods</p> <p>Fifty-two male patients with hyperthyroidism, 31 with Graves' disease (GD) and 21 with toxic multinodular goiter (TNG), with an age ranging from 23 to 65 years were included, together with 25 healthy euthyroid men with matched age as a control group. In addition to full clinical examination, patients and controls were subjected to measurement of BMD using dual-energy X-ray absorptiometery scanning of the lower half of the left radius. Also, some biochemical markers of bone turnover were done for all patients and controls.</p> <p>Results</p> <p>Biochemical markers of bone turnover: included serum bone specific alkaline phosphatase, osteocalcin, carboxy terminal telopeptide of type l collagen also, urinary deoxypyridinoline cross-links (DXP), urinary DXP/urinary creatinine ratio and urinary calcium/urinary creatinine ratio were significantly higher in patients with GD and TNG compared to controls (P < 0.01). However, there was non-significant difference in these parameters between GD and TNG patients (P > 0.05). BMD was significantly lower in GD and TNG compared to controls, but the Z-score of BMD at the lower half of the left radius in patients with GD (-1.7 ± 0.5 SD) was not significantly different from those with TNG (-1.6 ± 0.6 SD) (>0.05). There was significant positive correlation between free T3 and free T4 with biochemical markers of bone turnover, but negative correlation between TSH and those biochemical markers of bone turnover. The duration of the thyrotoxic state positively correlated with the assessed bone turnover markers, but it is negatively correlated with the Z-score of BMD in the studied hyperthyroid patients (r = -0.68, P < 0.0001).</p> <p>Conclusion</p> <p>Men with hyperthyroidism have significant bone loss with higher biochemical markers of bone turnover. The severity and the duration of the thyrotoxic state are directly related to the derangement of biochemical markers of bone turnover and bone loss.</p

The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy

Background: The ability to accurately predict operative duration has the potential to optimise theatre efficiency and utilisation, thus reducing costs and increasing staff and patient satisfaction. With laparoscopic cholecystectomy being one of the most commonly performed procedures worldwide, a tool to predict operative duration could be extremely beneficial to healthcare organisations. Methods: Data collected from the CholeS study on patients undergoing cholecystectomy in UK and Irish hospitals between 04/2014 and 05/2014 were used to study operative duration. A multivariable binary logistic regression model was produced in order to identify significant independent predictors of long (> 90 min) operations. The resulting model was converted to a risk score, which was subsequently validated on second cohort of patients using ROC curves. Results: After exclusions, data were available for 7227 patients in the derivation (CholeS) cohort. The median operative duration was 60 min (interquartile range 45–85), with 17.7% of operations lasting longer than 90 min. Ten factors were found to be significant independent predictors of operative durations > 90 min, including ASA, age, previous surgical admissions, BMI, gallbladder wall thickness and CBD diameter. A risk score was then produced from these factors, and applied to a cohort of 2405 patients from a tertiary centre for external validation. This returned an area under the ROC curve of 0.708 (SE = 0.013, p  90 min increasing more than eightfold from 5.1 to 41.8% in the extremes of the score. Conclusion: The scoring tool produced in this study was found to be significantly predictive of long operative durations on validation in an external cohort. As such, the tool may have the potential to enable organisations to better organise theatre lists and deliver greater efficiencies in care

Développement de méthodes à base de descripteurs locaux et apprentissage profond pour la reconnaissance du visage et des émotions faciales

The research objectives of this thesis concern the development of new concepts for image segmentation and region classification for image analysis. This involves implementing new descriptors, whether color, texture, or shape, to characterize regions and propose new deep learning architectures for the various applications linked to facial analysis. We restrict our focus on face recognition and person-independent facial expressions classification tasks, which are more challenging, especially in unconstrained environments. Our thesis lead to the proposal of many contributions related to facial analysis based on handcrafted and deep architecture.We contributed to face recognition by an effective local features descriptor referred to as Mixed Neighborhood Topology Cross Decoded Patterns (MNTCDP). Our face descriptor relies on a new neighborhood topology and a sophisticated kernel function that help to effectively encode the person-related features. We evaluated the proposed MNTCDP-based face recognition system according to well-known and challenging benchmarks of the state-of-the-art, covering individuals' diversity, uncontrolled environment, variable background and lighting conditions. The achieved results outperformed several state-of-the-art ones.As a second contribution, we handled the challenge of pose-invariant face recognition (PIFR) by developing a Generative Adversarial Network (GAN) based image translation to generate a frontal image corresponding to a profile one. Hence, this translation makes the recognition much easier since most reference databases include only frontal face samples. We made an End-to-End deep architecture that contains the GAN for translating profile samples and a ResNet-based classifier to identify the person from its synthesized frontal image. The experiments, which we conducted on an adequate dataset with respect to person-independent constraints between the training and testing, highlight significant improvement in the PIFR performance.Our contributions to the facial expression recognition task cover both static and dynamic-based scenarios. The static-based FER framework relies on extracting textural and shape features from specific face landmarks that carry enough information to detect the dominant emotion. We proposed a new descriptor referred to as Orthogonal and Parallel-based Directions Generic Query Map Binary Patterns (OPD-GQMBP) to efficiently extract emotion-related textural features from 49 landmarks (regions of 32 by 32 pixels). These features are combined with shape ones computed by using Histogram of Oriented Gradients (HOG) descriptor on a binary mask representing the interpolation of the 49 landmarks. The classification is done through the SVM classifier. The achieved Person-Independent performance on five benchmarks with respect to Leave One Subject Out protocol demonstrated the effectiveness of the overall proposed framework against deep and handcrafted state-of-the-art ones. On the other hand, dynamic FER contribution incorporates Long Term Short Memory (LSTM) deep network to encode the temporal information efficiently with a guiding attention map to focus on the emotion-related landmarks and guarantee the person-independent constraint. We considered four samples as inputs representing the evolution of the emotion to its peak. Each sample is encoded through a ResNet-based stream, and the four streams are joined by an LSTM block that predicts the dominant emotion. The experiments conducted on three datasets for dynamic FER showed that the proposed deep CNN-LSTM architecture outperforms the state-of-the-art.Cette thèse porte sur le développement de nouveaux concepts de segmentation d'images et de classification de régions pour l'analyse d'images. Il s'agit de mettre en œuvre de nouveaux descripteurs, qu'ils soient de couleur, de texture ou de forme et proposer de nouvelles architectures d'apprentissage profond pour des applications liées à l'analyse faciale. Nous nous concentrons sur la reconnaissance faciale et la classification des expressions faciales.Notre thèse a débouché sur la proposition de nombreuses contributions liées à l'analyse faciale couvrant des architectures classiques et profondes.Nous avons contribué à la reconnaissance faciale tout d’abord par la proposition d’un descripteur local appelé Mixed Neighborhood Topology Cross Decoded Patterns. Notre descripteur de visage repose sur une nouvelle topologie de voisinage et une fonction de noyau avancée permettant en encodage efficace des caractéristiques liées à la personne. Nous avons évalué le système de reconnaissance faciale proposé à base du MNTCDP sur des bases de données connues de l'état de l'art, disposant de challenges, couvrant la diversité des individus, environnement non contrôlé, des conditions de fond et d'éclairage variables. Les résultats obtenus ont dépassé plusieurs résultats de l’état de l’art.Pour la deuxième contribution, nous avons relevé le défi de la reconnaissance faciale invariante aux poses (PIFR) en développant une méthode de génération d'images basée sur le Generative Adversarial Network, afin de générer une image frontale correspondant à une image en profil. Cette transformation rend la reconnaissance beaucoup plus facile puisque la plupart des bases de données de référence n'incluent que des échantillons de face frontale. Nous avons créé une architecture profonde de bout en bout, composé du GAN pour la génération des échantillons de profil et un classificateur basé sur ResNet pour l’identification de la personne à partir de son image frontale synthétisée. Les expériences, que nous avons menées sur une base de données adéquate, notamment en termes de chevauchement des individus entre la base de l’apprentissage et celle de l’évaluation, mettent en évidence une grande amélioration des performances du PIFR grâce à la génération d’images frontales du GAN.Nos contributions à la tâche de reconnaissance des expressions faciales (FER) couvrent à la fois des scénarios statiques (une image) et dynamiques (plusieurs images). Notre architecture pour FER avec le mode statique repose sur l'extraction de caractéristiques de texture et de forme à partir de points de repère du visage spécifiques qui présentent suffisamment d'informations pour détecter l'émotion dominante. Nous avons proposé un descripteur appelé Orthogonal and Parallel-based Directions Generic Query Map Binary Patterns pour extraire efficacement les caractéristiques texturales liées aux émotions à partir de 49 points de repère. Ces caractéristiques sont combinées avec celles à base de forme calculées à l'aide du descripteur HOG sur un masque binaire représentant l'interpolation des 49 points. La classification est réalisée via le SVM. Less performances obtenues sur cinq bases de données avec le protocole Leave One Subject Out ont démontré l'efficacité de l’architecture proposée par rapport à l’état de l’art. D'autre part, notre contribution relative à la FER avec le mode dynamique intègre un réseau LSTM pour encoder avec précision les informations temporelles avec un masque d'attention permettant de se concentrer sur les repères liés aux émotions et garantir la robustesse de la reconnaissance. Nous avons considéré quatre échantillons comme entrées représentant l'évolution de l'émotion jusqu'à son pic. Chaque échantillon est codé via une branche CNN et les quatre branches sont jointes par un bloc LSTM qui prédit l'émotion dominante. Les expériences menées sur trois bases de données pour FER dynamique ont montré que l'architecture CNN-LSTM profonde proposée dépasse l'état de l'art

Développement de méthodes à base de descripteurs locaux et apprentissage profond pour la reconnaissance du visage et des émotions faciales

The research objectives of this thesis concern the development of new concepts for image segmentation and region classification for image analysis. This involves implementing new descriptors, whether color, texture, or shape, to characterize regions and propose new deep learning architectures for the various applications linked to facial analysis. We restrict our focus on face recognition and person-independent facial expressions classification tasks, which are more challenging, especially in unconstrained environments. Our thesis lead to the proposal of many contributions related to facial analysis based on handcrafted and deep architecture.We contributed to face recognition by an effective local features descriptor referred to as Mixed Neighborhood Topology Cross Decoded Patterns (MNTCDP). Our face descriptor relies on a new neighborhood topology and a sophisticated kernel function that help to effectively encode the person-related features. We evaluated the proposed MNTCDP-based face recognition system according to well-known and challenging benchmarks of the state-of-the-art, covering individuals' diversity, uncontrolled environment, variable background and lighting conditions. The achieved results outperformed several state-of-the-art ones.As a second contribution, we handled the challenge of pose-invariant face recognition (PIFR) by developing a Generative Adversarial Network (GAN) based image translation to generate a frontal image corresponding to a profile one. Hence, this translation makes the recognition much easier since most reference databases include only frontal face samples. We made an End-to-End deep architecture that contains the GAN for translating profile samples and a ResNet-based classifier to identify the person from its synthesized frontal image. The experiments, which we conducted on an adequate dataset with respect to person-independent constraints between the training and testing, highlight significant improvement in the PIFR performance.Our contributions to the facial expression recognition task cover both static and dynamic-based scenarios. The static-based FER framework relies on extracting textural and shape features from specific face landmarks that carry enough information to detect the dominant emotion. We proposed a new descriptor referred to as Orthogonal and Parallel-based Directions Generic Query Map Binary Patterns (OPD-GQMBP) to efficiently extract emotion-related textural features from 49 landmarks (regions of 32 by 32 pixels). These features are combined with shape ones computed by using Histogram of Oriented Gradients (HOG) descriptor on a binary mask representing the interpolation of the 49 landmarks. The classification is done through the SVM classifier. The achieved Person-Independent performance on five benchmarks with respect to Leave One Subject Out protocol demonstrated the effectiveness of the overall proposed framework against deep and handcrafted state-of-the-art ones. On the other hand, dynamic FER contribution incorporates Long Term Short Memory (LSTM) deep network to encode the temporal information efficiently with a guiding attention map to focus on the emotion-related landmarks and guarantee the person-independent constraint. We considered four samples as inputs representing the evolution of the emotion to its peak. Each sample is encoded through a ResNet-based stream, and the four streams are joined by an LSTM block that predicts the dominant emotion. The experiments conducted on three datasets for dynamic FER showed that the proposed deep CNN-LSTM architecture outperforms the state-of-the-art.Cette thèse porte sur le développement de nouveaux concepts de segmentation d'images et de classification de régions pour l'analyse d'images. Il s'agit de mettre en œuvre de nouveaux descripteurs, qu'ils soient de couleur, de texture ou de forme et proposer de nouvelles architectures d'apprentissage profond pour des applications liées à l'analyse faciale. Nous nous concentrons sur la reconnaissance faciale et la classification des expressions faciales.Notre thèse a débouché sur la proposition de nombreuses contributions liées à l'analyse faciale couvrant des architectures classiques et profondes.Nous avons contribué à la reconnaissance faciale tout d’abord par la proposition d’un descripteur local appelé Mixed Neighborhood Topology Cross Decoded Patterns. Notre descripteur de visage repose sur une nouvelle topologie de voisinage et une fonction de noyau avancée permettant en encodage efficace des caractéristiques liées à la personne. Nous avons évalué le système de reconnaissance faciale proposé à base du MNTCDP sur des bases de données connues de l'état de l'art, disposant de challenges, couvrant la diversité des individus, environnement non contrôlé, des conditions de fond et d'éclairage variables. Les résultats obtenus ont dépassé plusieurs résultats de l’état de l’art.Pour la deuxième contribution, nous avons relevé le défi de la reconnaissance faciale invariante aux poses (PIFR) en développant une méthode de génération d'images basée sur le Generative Adversarial Network, afin de générer une image frontale correspondant à une image en profil. Cette transformation rend la reconnaissance beaucoup plus facile puisque la plupart des bases de données de référence n'incluent que des échantillons de face frontale. Nous avons créé une architecture profonde de bout en bout, composé du GAN pour la génération des échantillons de profil et un classificateur basé sur ResNet pour l’identification de la personne à partir de son image frontale synthétisée. Les expériences, que nous avons menées sur une base de données adéquate, notamment en termes de chevauchement des individus entre la base de l’apprentissage et celle de l’évaluation, mettent en évidence une grande amélioration des performances du PIFR grâce à la génération d’images frontales du GAN.Nos contributions à la tâche de reconnaissance des expressions faciales (FER) couvrent à la fois des scénarios statiques (une image) et dynamiques (plusieurs images). Notre architecture pour FER avec le mode statique repose sur l'extraction de caractéristiques de texture et de forme à partir de points de repère du visage spécifiques qui présentent suffisamment d'informations pour détecter l'émotion dominante. Nous avons proposé un descripteur appelé Orthogonal and Parallel-based Directions Generic Query Map Binary Patterns pour extraire efficacement les caractéristiques texturales liées aux émotions à partir de 49 points de repère. Ces caractéristiques sont combinées avec celles à base de forme calculées à l'aide du descripteur HOG sur un masque binaire représentant l'interpolation des 49 points. La classification est réalisée via le SVM. Less performances obtenues sur cinq bases de données avec le protocole Leave One Subject Out ont démontré l'efficacité de l’architecture proposée par rapport à l’état de l’art. D'autre part, notre contribution relative à la FER avec le mode dynamique intègre un réseau LSTM pour encoder avec précision les informations temporelles avec un masque d'attention permettant de se concentrer sur les repères liés aux émotions et garantir la robustesse de la reconnaissance. Nous avons considéré quatre échantillons comme entrées représentant l'évolution de l'émotion jusqu'à son pic. Chaque échantillon est codé via une branche CNN et les quatre branches sont jointes par un bloc LSTM qui prédit l'émotion dominante. Les expériences menées sur trois bases de données pour FER dynamique ont montré que l'architecture CNN-LSTM profonde proposée dépasse l'état de l'art

Développement de méthodes à base de descripteurs locaux et apprentissage profond pour la reconnaissance du visage et des émotions faciales

Cette thèse porte sur le développement de nouveaux concepts de segmentation d'images et de classification de régions pour l'analyse d'images. Il s'agit de mettre en œuvre de nouveaux descripteurs, qu'ils soient de couleur, de texture ou de forme et proposer de nouvelles architectures d'apprentissage profond pour des applications liées à l'analyse faciale. Nous nous concentrons sur la reconnaissance faciale et la classification des expressions faciales.Notre thèse a débouché sur la proposition de nombreuses contributions liées à l'analyse faciale couvrant des architectures classiques et profondes.Nous avons contribué à la reconnaissance faciale tout d’abord par la proposition d’un descripteur local appelé Mixed Neighborhood Topology Cross Decoded Patterns. Notre descripteur de visage repose sur une nouvelle topologie de voisinage et une fonction de noyau avancée permettant en encodage efficace des caractéristiques liées à la personne. Nous avons évalué le système de reconnaissance faciale proposé à base du MNTCDP sur des bases de données connues de l'état de l'art, disposant de challenges, couvrant la diversité des individus, environnement non contrôlé, des conditions de fond et d'éclairage variables. Les résultats obtenus ont dépassé plusieurs résultats de l’état de l’art.Pour la deuxième contribution, nous avons relevé le défi de la reconnaissance faciale invariante aux poses (PIFR) en développant une méthode de génération d'images basée sur le Generative Adversarial Network, afin de générer une image frontale correspondant à une image en profil. Cette transformation rend la reconnaissance beaucoup plus facile puisque la plupart des bases de données de référence n'incluent que des échantillons de face frontale. Nous avons créé une architecture profonde de bout en bout, composé du GAN pour la génération des échantillons de profil et un classificateur basé sur ResNet pour l’identification de la personne à partir de son image frontale synthétisée. Les expériences, que nous avons menées sur une base de données adéquate, notamment en termes de chevauchement des individus entre la base de l’apprentissage et celle de l’évaluation, mettent en évidence une grande amélioration des performances du PIFR grâce à la génération d’images frontales du GAN.Nos contributions à la tâche de reconnaissance des expressions faciales (FER) couvrent à la fois des scénarios statiques (une image) et dynamiques (plusieurs images). Notre architecture pour FER avec le mode statique repose sur l'extraction de caractéristiques de texture et de forme à partir de points de repère du visage spécifiques qui présentent suffisamment d'informations pour détecter l'émotion dominante. Nous avons proposé un descripteur appelé Orthogonal and Parallel-based Directions Generic Query Map Binary Patterns pour extraire efficacement les caractéristiques texturales liées aux émotions à partir de 49 points de repère. Ces caractéristiques sont combinées avec celles à base de forme calculées à l'aide du descripteur HOG sur un masque binaire représentant l'interpolation des 49 points. La classification est réalisée via le SVM. Less performances obtenues sur cinq bases de données avec le protocole Leave One Subject Out ont démontré l'efficacité de l’architecture proposée par rapport à l’état de l’art. D'autre part, notre contribution relative à la FER avec le mode dynamique intègre un réseau LSTM pour encoder avec précision les informations temporelles avec un masque d'attention permettant de se concentrer sur les repères liés aux émotions et garantir la robustesse de la reconnaissance. Nous avons considéré quatre échantillons comme entrées représentant l'évolution de l'émotion jusqu'à son pic. Chaque échantillon est codé via une branche CNN et les quatre branches sont jointes par un bloc LSTM qui prédit l'émotion dominante. Les expériences menées sur trois bases de données pour FER dynamique ont montré que l'architecture CNN-LSTM profonde proposée dépasse l'état de l'art.The research objectives of this thesis concern the development of new concepts for image segmentation and region classification for image analysis. This involves implementing new descriptors, whether color, texture, or shape, to characterize regions and propose new deep learning architectures for the various applications linked to facial analysis. We restrict our focus on face recognition and person-independent facial expressions classification tasks, which are more challenging, especially in unconstrained environments. Our thesis lead to the proposal of many contributions related to facial analysis based on handcrafted and deep architecture.We contributed to face recognition by an effective local features descriptor referred to as Mixed Neighborhood Topology Cross Decoded Patterns (MNTCDP). Our face descriptor relies on a new neighborhood topology and a sophisticated kernel function that help to effectively encode the person-related features. We evaluated the proposed MNTCDP-based face recognition system according to well-known and challenging benchmarks of the state-of-the-art, covering individuals' diversity, uncontrolled environment, variable background and lighting conditions. The achieved results outperformed several state-of-the-art ones.As a second contribution, we handled the challenge of pose-invariant face recognition (PIFR) by developing a Generative Adversarial Network (GAN) based image translation to generate a frontal image corresponding to a profile one. Hence, this translation makes the recognition much easier since most reference databases include only frontal face samples. We made an End-to-End deep architecture that contains the GAN for translating profile samples and a ResNet-based classifier to identify the person from its synthesized frontal image. The experiments, which we conducted on an adequate dataset with respect to person-independent constraints between the training and testing, highlight significant improvement in the PIFR performance.Our contributions to the facial expression recognition task cover both static and dynamic-based scenarios. The static-based FER framework relies on extracting textural and shape features from specific face landmarks that carry enough information to detect the dominant emotion. We proposed a new descriptor referred to as Orthogonal and Parallel-based Directions Generic Query Map Binary Patterns (OPD-GQMBP) to efficiently extract emotion-related textural features from 49 landmarks (regions of 32 by 32 pixels). These features are combined with shape ones computed by using Histogram of Oriented Gradients (HOG) descriptor on a binary mask representing the interpolation of the 49 landmarks. The classification is done through the SVM classifier. The achieved Person-Independent performance on five benchmarks with respect to Leave One Subject Out protocol demonstrated the effectiveness of the overall proposed framework against deep and handcrafted state-of-the-art ones. On the other hand, dynamic FER contribution incorporates Long Term Short Memory (LSTM) deep network to encode the temporal information efficiently with a guiding attention map to focus on the emotion-related landmarks and guarantee the person-independent constraint. We considered four samples as inputs representing the evolution of the emotion to its peak. Each sample is encoded through a ResNet-based stream, and the four streams are joined by an LSTM block that predicts the dominant emotion. The experiments conducted on three datasets for dynamic FER showed that the proposed deep CNN-LSTM architecture outperforms the state-of-the-art

RGBD deep multi-scale network for background subtraction

International audienceThis paper proposes a novel deep learning model called deep multi-scale network (DMSN) for background subtraction. This convolutional neural network is built to use RGB color channels and Depth maps as inputs with which it can fuse semantic and spatial information. In comparison with previous deep learning background subtraction techniques that lack information due to its use of only RGB channels, our RGBD version is able to overcome most of the drawbacks, especially in some particular kinds of challenges. Further, this paper introduces a new protocol for the SBM-RGBD dataset, concerning scene-independent evaluation, dedicated to Deep Learning methods to set up a competitive platform that includes more challenging situations. The proposed method proved its efficiency in solving the background subtraction in complex situations at different levels. The experimental results verify that the proposed work outperforms the state of the art on SBM-RGBD and GSM datasets

Practical solutions for including Sex As a Biological Variable (SABV) in preclinical neuropsychopharmacological research.

Recently, many funding agencies have released guidelines on the importance of considering sex as a biological variable (SABV) as an experimental factor, aiming to address sex differences and avoid possible sex biases to enhance the reproducibility and translational relevance of preclinical research. In neuroscience and pharmacology, the female sex is often omitted from experimental designs, with researchers generalizing male-driven outcomes to both sexes, risking a biased or limited understanding of disease mechanisms and thus potentially ineffective therapeutics. Herein, we describe key methodological aspects that should be considered when sex is factored into in vitro and in vivo experiments and provide practical knowledge for researchers to incorporate SABV into preclinical research. Both age and sex significantly influence biological and behavioral processes due to critical changes at different timepoints of development for males and females and due to hormonal fluctuations across the rodent lifespan. We show that including both sexes does not require larger sample sizes, and even if sex is included as an independent variable in the study design, a moderate increase in sample size is sufficient. Moreover, the importance of tracking hormone levels in both sexes and the differentiation between sex differences and sex-related strategy in behaviors are explained. Finally, the lack of robust data on how biological sex influences the pharmacokinetic (PK), pharmacodynamic (PD), or toxicological effects of various preclinically administered drugs to animals due to the exclusion of female animals is discussed, and methodological strategies to enhance the rigor and translational relevance of preclinical research are proposed