Transferring facial pose and expression features from one face to another is a challenging problem and an interesting topic in pattern recognition, but is one of great importance with many applications. However, existing models usually learn to trans...
An essential aspect in the interaction between people and computers is the recognition of facial expressions. A key issue in this process is to select relevant features to classify facial expressions accurately. This study examines the selection of o...
In the pattern recognition domain, deep architectures are currently widely used and they have achieved fine results. However, these deep architectures make particular demands, especially in terms of their requirement for big datasets and GPU. Aiming...
Several studies on micro-expression recognition have contributed mainly to accuracy improvement. However, the computational complexity receives lesser attention comparatively and therefore increases the cost of micro-expression recognition for real-t...
The availability of multiple wheat genome sequences enables us to identify core genes and characterize their genetic and epigenetic features, thereby advancing our understanding of their biological implications within individual plant species. It is,...
Recognizing facial expression has attracted much more attention due to its broad range of applications in human–computer interaction systems. Although facial representation is crucial to final recognition accuracy, traditional handcrafted repre...
We propose a method for dynamic facial expression recognition that integrates three complementary feature streams from video sequences: (1) raw texture features extracted with EfficientNet-B0, (2) deep geometric features from face mesh representation...
Nowadays, the facial expression recognition (FER) task has transitioned from a laboratory-controlled scenario to in-the-wild conditions. However, recognizing facial expressions in the wild is challenging due to factors such as variant backgrounds, lo...
The performance of a facial expression recognition network degrades obviously under situations of uneven illumination or partial occluded face as it is quite difficult to pinpoint the attention hotspots on the dynamically changing regions (e.g., eyes...
Single-cell RNA sequencing (scRNA-seq) studies increasingly combine expression-based and mutation-derived signals, but small-cohort designs with repeated runs from the same biological unit can make standard cross-validation overly optimistic. We rean...
In farmed animals, body color is not only an ecological trait but also an important trait that influences the commercial value of the animals. Melanin plays an important role in the formation of body color in animals, while the tyrosinase (TYR)...
In the era of artificial intelligence, accomplishing emotion recognition in human–computer interaction is a key work. Expressions contain plentiful information about human emotion. We found that the canny edge detector can significantly help im...
Facial expression plays an important role in human–computer interaction and affective computing. However, existing expression recognition methods cannot effectively capture multi-scale structural details contained in facial expressions, leading...
Micro-expressions are extremely subtle and short-lived facial muscle movements that often reveal an individual’s genuine emotions. However, micro-expression recognition (MER) remains highly challenging due to its short duration, low motion inte...
In this paper, a novel approach to facial expression recognition based on the discrete separable shearlet transform (DSST) and normalized mutual information feature selection is proposed. The approach can be divided into five steps. First, all test a...
In smart classroom environments, accurately recognizing students’ facial expressions is crucial for teachers to efficiently assess students’ learning states, timely adjust teaching strategies, and enhance teaching quality and effectiveness. In this p...
Facial expression methods play a vital role in human–computer interaction and other fields, but there are factors such as occlusion, illumination, and pose changes in wild facial recognition, as well as category imbalances between different dat...
The expression abundance of transcripts in nondiseased breast tissue varies among individuals. The association study of genotypes and imaging phenotypes may help us to understand this individual variation. Since existing reports mainly focus on tumor...
When micro-expressions are mixed with normal or macro-expressions, it becomes increasingly challenging to spot them in long videos. Aiming at the specific time prior of micro-expressions (MEs), an ME spotting network called AEM-Net (adaptive enhanced...
Micro-expressions, which are spontaneous and difficult to suppress, reveal a person’s true emotions. They are characterized by short duration and low intensity, making the task of micro-expression recognition challenging in the field of emotion...
In recent years, significant progress has been made in facial expression recognition methods. However, tasks related to facial expression recognition in real environments still require further research. This paper proposes a tri-cross-attention trans...
The emotional well-being of a child is crucial for their successful integration into society as a productive individual. While technology has made significant strides in enabling machines to decipher human emotional signals, current research in emoti...
Facial expression recognition (FER) is a key task in affective computing and human–computer interaction, aiming to decode facial muscle movements into emotional categories. Although deep learning-based FER has achieved remarkable progress, robu...
Recent advances in deep learning have significantly advanced micro-expression recognition, yet most existing methods process the entire facial region holistically, struggling to capture subtle variations in facial action units, which limits recogniti...
Facial-expression recognition (FER) on FER2013 remains challenging because of low-resolution images, class imbalance, and label ambiguity. This study presents a global–local feature-fusion framework that integrates complementary representations...
Micro-expressions (MEs) are involuntary facial movements that reveal genuine emotions, holding significant value in fields like deception detection and psychological diagnosis. However, micro-expression recognition (MER) is fundamentally challenged b...
Facial expression recognition (FER) is a core technology that enables computers to understand and react to human emotions. In particular, the use of face alignment algorithms as a preprocessing step in image-based FER is important for accurately norm...
The objective of this work is to analyze which features are most important in the recognition of facial expressions. To achieve this, we built a facial expression recognition system that learns from a controlled capture data set. The system uses diff...
Automatic Facial Expression Recognition (AFER) is a key component of affective computing, enabling machines to recognize and interpret human emotions across various applications such as human–computer interaction, healthcare, entertainment, and...
As people communicate with each other, they use gestures and facial expressions as a means to convey and understand emotional state. Non-verbal means of communication are essential to understanding, based on external clues to a person’s emotion...
Compelling facial expression recognition (FER) processes have been utilized in very successful fields like computer vision, robotics, artificial intelligence, and dynamic texture recognition. However, the FER’s critical problem with traditional...
The development of Facial Expression Recognition (FER) technology has significantly enhanced the naturalness and intuitiveness of human-robot interaction. In the field of service robots, particularly in applications such as production assistance, car...
High-dimensional gene expression data pose computational challenges for feature selection due to redundancy, noise, and limited sample sizes. Recursive Cluster Elimination (RCE) addresses these issues through iterative feature clustering and eliminat...
Gene expression programming (GEP) is frequently used to create intelligent dispatching rules for job-shop scheduling. The proper selection of the terminal set is a critical factor for the success of GEP. However, there are various job features and ma...
(1) Background: Gene-expression data usually contain missing values (MVs). Numerous methods focused on how to estimate MVs have been proposed in the past few years. Recent studies show that those imputation algorithms made little difference in classi...
Large-scale facial expression datasets are primarily composed of real-world facial expressions. Expression occlusion and large-angle faces are two important problems affecting the accuracy of expression recognition. Moreover, because facial expressio...
Micro-expressions are a type of real emotional expression, which are unconscious and difficult to hide. Identifying these expressions has great potential applications in areas such as civil aviation security, criminal interrogation, and clinical medi...
The biological significance of the CD38 molecule goes beyond metabolic, enzymatic, and proliferative functions. CD38 possesses the functions of an exoenzyme and receptor, and is actively involved in the mechanisms of adhesion, migration, intercellula...
Androgen receptor (AR) expression is frequently observed in breast cancer, but its association with estrogen receptor (ER) expression in breast cancer remains unclear. This study analyzed the clinicopathological and molecular features associated with...
This paper addresses the problem of Facial Expression Recognition (FER), focusing on unobvious facial movements. Traditional methods often cause overfitting problems or incomplete information due to insufficient data and manual selection of features....
Background: Endometrial cancer (EC) has robust molecular diagnostic evidence that correlates well with prognosis. In various types of cancers, FcRn has been identified as an early marker for prognosis. This study aims to assess FcRn expression and it...
Lung adenocarcinoma (LUAD) is the most common type of lung cancer and a leading cause of cancer-related deaths worldwide. Despite important recent advances, the prognosis for LUAD patients is still unfavourable, with a 5 year-survival rate close to 1...
Dynamic Facial Expression Recognition (DFER) has recently attracted significant interest due to its vital role in enabling empathetic and human-compatible technologies. Developing models that remain robust under in-the-wild variability is a key motiv...
Background: Dermatofibromas (DFs) are benign fibrohistiocytic lesions that usually do not express CD34 protein. This study aimed to analyze the literature concerning the immunohistological and ultrastructural features of DFs. It also related these fe...
Aberrant angiogenesis is a pathological feature of a number of diseases and arises from the uncoordinated expression of angiogenic factors as response to different cellular stresses. Age-related macular degeneration (AMD), a leading cause of vision l...
KAP1 is an universal corepressor for Kruppel-associated box zinc finger proteins in both normal and tumor cells. In this study, the biological function and clinical significance of KAP1 expression in ovarian cancer were investigated. Immunohistologic...
Background: Hypoxia-regulated proteins (HIF-1α and GLUT-1) have been identified as prognostic markers in various cancers; however, their role in endometrial cancer remains unclear. This study aimed to evaluate HIF-1α and GLUT-1 expression...
CD47, a transmembrane protein, is widely overexpressed on the tumor cell surface. However, the prognostic significance of CD47 expression in colorectal adenocarcinoma (CRA) has not yet been clarified. Here, we investigated the clinicopathologic signi...
Intrahepatic cholangiocarcinoma (ICC) is a heterogeneous group of cancers of the intrahepatic biliary tract. However, few studies have evaluated integrin expression according to an ICC subgroup. We immunohistochemically investigated α6β4 (β4) and αvβ...
Loss of the polybromo-1 (PBRM1) protein has been expected as a possible biomarker for clear cell renal cell carcinoma (ccRCC). There is little knowledge about how PBRM1 immunohistochemical expression correlates with the histomorphological features of...
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