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Deep learning has been revolutionizing many fields in computer vision, and facial informatics is one of the major fields. Novel approaches and performance breakthroughs are often reported on existing benchmarks. As the performances on existing benchmarks are close to saturation, larger and more challenging databases are being made and considered as new benchmarks, further pushing the advancement of the technologies. Considering face recognition, for example, the VGG-Face2 and Dual-Agent GAN report nearly perfect and better-than-human performances on the IARPA Janus Benchmark A (IJB-A) benchmark. More challenging benchmarks, e.g., the IARPA Janus Benchmark A (IJB-C), QMUL-SurvFace and MegaFace, are accepted as new standards for evaluating the performance of a new approach. Such an evolution is also seen in other branches of face informatics. In this Special Issue, we have selected the papers that report the latest progresses made in the following topics: 1. Face liveness detection 2. Emotion classification 3. Facial age estimation 4. Facial landmark detection We are hoping that this Special Issue will be beneficial to all fields of facial informatics.

History of engineering & technology --- deep learning --- RGB --- depth --- facial landmarking --- merging networks --- 3D geometry data --- 2D attribute maps --- fused CNN feature --- coarse-to-fine --- convolutional neural network (CNN) --- deep metric learning --- multi-task learning --- image classification --- age estimation --- generative adversarial network --- emotion classification --- facial key point detection --- facial images processing --- convolutional neural networks --- face liveness detection --- convolutional neural network --- thermal image --- external knowledge

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New information and knowledge are important aspects of innovation in modern farming systems. There is currently an abundance of digital and data-driven solutions that can potentially transform our food systems. At a time when the general public has concerns about how food is produced and the impact of farm production systems on the environment, strategies to increase public acceptance and the sustainability of food production are required more than ever. New tools and technology can provide timely insights into aspects such as nutrient profiles, the tracking of animal or plant wellbeing, and land-use options to enhance inputs and outputs associated with the farm business. Such solutions have the ultimate aim of enhancing production efficiency and contributing to the process of learning about the advantages of the innovation, while ensuring more sustainable food supplies. At the farm level, any new information needs to be in a useful format and beneficial for management and farm decision-making. The papers in this Special Issue evaluate agri-business innovation that can enhance farm-level decision-making.

Research & information: general --- Biology, life sciences --- Technology, engineering, agriculture --- dairy cows --- computer vision --- behaviors --- monitoring --- management --- behavior --- birth --- observations --- sheep --- proximal --- sensing --- LiDAR --- photogrammetry --- grasslands --- pastures --- Adversarial-VAE --- tomato leaf disease identification --- image generation --- convolutional neural network --- potato management --- tuber formation stage --- precipitation patterns

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Radiomics is one of the most successful branches of research in the field of image processing and analysis, as it provides valuable quantitative information for the personalized medicine. It has the potential to discover features of the disease that cannot be appreciated with the naked eye in both preclinical and clinical studies. In general, all quantitative approaches based on biomedical images, such as positron emission tomography (PET), computed tomography (CT) and magnetic resonance imaging (MRI), have a positive clinical impact in the detection of biological processes and diseases as well as in predicting response to treatment. This Special Issue, “Image Processing and Analysis for Preclinical and Clinical Applications”, addresses some gaps in this field to improve the quality of research in the clinical and preclinical environment. It consists of fourteen peer-reviewed papers covering a range of topics and applications related to biomedical image processing and analysis.

Research & information: general --- Chemistry --- deep learning --- segmentation --- prostate --- MRI --- ENet --- UNet --- ERFNet --- radiomics --- gamma knife --- imaging quantification --- [11C]-methionine positron emission tomography --- cancer --- atrial fibrillation --- 4D-flow --- stasis --- pulmonary vein ablation --- convolutional neural network --- transfer learning --- maxillofacial fractures --- computed tomography images --- radiography --- xenotransplant --- cancer cells --- zebrafish image analysis --- in vivo assay --- convolutional neural network (CNN) --- magnetic resonance imaging (MRI) --- neoadjuvant chemoradiation therapy (nCRT) --- pathologic complete response (pCR) --- rectal cancer --- radiomics feature robustness --- PET/MRI co-registration --- image registration --- fundus image --- feature extraction --- glomerular filtration rate --- Gate’s method --- renal depth --- computed tomography --- computer-aided diagnosis --- medical-image analysis --- automated prostate-volume estimation --- abdominal ultrasound images --- image-patch voting --- soft tissue sarcoma --- volume estimation --- artificial intelligence --- Basal Cell Carcinoma --- skin lesion --- classification --- colon --- positron emission tomography-computed tomography --- nuclear medicine --- image pre-processing --- high-level synthesis --- X-ray pre-processing --- pipelined architecture --- n/a --- Gate's method

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Biomedical image processing is an interdisciplinary field involving a variety of disciplines, e.g., electronics, computer science, physics, mathematics, physiology, and medicine. Several imaging techniques have been developed, providing many approaches to the study of the human body. Biomedical image processing is finding an increasing number of important applications in, for example, the study of the internal structure or function of an organ and the diagnosis or treatment of a disease. If associated with classification methods, it can support the development of computer-aided diagnosis (CAD) systems, which could help medical doctors in refining their clinical picture.

Technology: general issues --- MR brain segmentation --- fuzzy clustering --- object extraction --- silhouette analysis --- DICOM processing --- 3D modeling --- semantic segmentation --- convolutional neural networks --- kidney biopsy --- kidney transplantation --- glomerulus detection --- glomerulosclerosis --- pattern recognition --- hemoglobin --- anemia --- human tissues --- conjunctiva --- non-invasive medical device --- training size --- deep learning --- convolutional neural network --- U-Net --- segmentation --- artificial intelligence --- digital pathology --- kidney fibrosis --- blood vessel segmentation --- inferior vena cava --- ultrasound imaging --- binary tree model --- pulsatility --- fluid volume assessment --- n/a

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Sentiment analysis is a branch of natural language processing concerned with the study of the intensity of the emotions expressed in a piece of text. The automated analysis of the multitude of messages delivered through social media is one of the hottest research fields, both in academy and in industry, due to its extremely high potential applicability in many different domains. This Special Issue describes both technological contributions to the field, mostly based on deep learning techniques, and specific applications in areas like health insurance, gender classification, recommender systems, and cyber aggression detection.

opinion mining --- affect computing --- health insurance --- Twitter --- hybrid vectorization --- violence against women --- word association --- collaborative schemes of sentiment analysis and sentiment systems --- random forest --- cyber-aggression --- deep learning --- online review --- emotion analysis --- lexicon construction --- provider networks --- text mining --- sentiment lexicon --- social media --- sentiment-aware word embedding --- psychographic segmentation --- medical web forum --- gender classification --- racism --- sentiment analysis --- sentiment classification --- sentiment word analysis --- social networks --- convolutional neural network --- review data mining --- machine learning --- emotion classification --- big data-driven marketing --- text feature representation --- recommender system --- user preference prediction --- violence based on sexual orientation --- semantic networks