High-resolution Imaging Research Articles

A high-performance sub-THz planar antenna array for THz sensing and imaging applications

Terahertz (THz) spectral region from 0.1 to 3 THz is envisaged to hold immense potential in the next generation of wireless technologies. Recently, research has focused on this terahertz gap, because of its unprecedented channel capacities. At the physical layer, the design complexities and fabrication of THz devices, especially antennas are the prime bottlenecks to realize its full potential. This article introduces a cost-effective, easy-to-fabricate, and reproducible sub-THz antenna design based on a single-layer planar printed circuit board technology. The antenna incorporates carefully designed quasi-cross slots and applied machine learning-assisted global optimization techniques to achieve the desired performance metrics. The antenna performance is elucidated through numerical simulations and verified through a rigorous in-house THz experimental framework around 100–110 GHz. The proposed antenna offers a peak gain of 13.90 dBi with less than 1 dB variation within the entire band of 100–110 GHz. The antenna holds the potential to achieve terabits per second data rates and futuristic high-resolution short-range THz imaging applications.

High-resolution three-dimensional imaging of topological textures in nanoscale single-diamond networks.

Topological defects-extended lattice deformations that are robust against local defects and annealing-have been exploited to engineer novel properties in both hard and soft materials. Yet, their formation kinetics and nanoscale three-dimensional structure are poorly understood, impeding their benefits for nanofabrication. We describe the fabrication of a pair of topological defects in the volume of a single-diamond network (space group Fd m) templated into gold from a triblock terpolymer crystal. Using X-ray nanotomography, we resolve the three-dimensional structure of nearly 70,000 individual single-diamond unit cells with a spatial resolution of 11.2 nm, allowing analysis of the long-range order of the network. The defects observed morphologically resemble the comet and trefoil patterns of equal and opposite half-integer topological charges observed in liquid crystals. Yet our analysis of strain in the network suggests typical hard matter behaviour. Our analysis approach does not require a priori knowledge of the expected positions of the nodes in three-dimensional nanostructured systems, allowing the identification of distorted morphologies and defects in large samples.

High-resolution defect imaging of composites using delay-sum-and-square beamforming algorithm

High-resolution defect imaging of composites using delay-sum-and-square beamforming algorithm

Heterodyne High-Harmonic Electrostatic Force Microscopy with Improved Spatial Resolution for Nanoscale Identification of Metallic/Semiconducting Carbon Nanotubes.

There are two main types of carbon nanotubes (CNTs): metallic and semiconducting. Naturally grown CNTs are randomly distributed, posing challenges in distinguishing between the two types. Here, a novel approach for nanoscale high-resolution imaging and identification of CNTs was introduced by incorporating the heterodyne technique into high-harmonic electrostatic force microscopy (HH-EFM) on an atomic force microscopy (AFM) platform. In the developed heterodyne HH-EFM, a more localized high-order gradient of tip-sample nonlinear interaction force is used as signal channels, resulting in an improved spatial resolution, compared to the conventional HH-EFM. Furthermore, the heterodyne HH-EFM also has the capability to visualize material carrier density and assess qualitative carrier transport performance. Our work not only presents a new approach to identifying/exploring electrical properties of low-dimensional nanomaterials but also provides a solution for optimizing resolution in long-range interaction-based functional AFM technologies.

FEditNet++: Few-Shot Editing of Latent Semantics in GAN Spaces with Correlated Attribute Disentanglement.

Generative Adversarial Networks have achieved significant advancements in generating and editing high-resolution images. However, most methods suffer from either requiring extensive labeled datasets or strong prior knowledge. It is also challenging for them to disentangle correlated attributes with few-shot data. In this paper, we propose FEditNet++, a GAN-based approach to explore latent semantics. It aims to enable attribute editing with limited labeled data and disentangle the correlated attributes. We propose a layer-wise feature contrastive objective, which takes into consideration content consistency and facilitates the invariance of the unrelated attributes before and after editing. Furthermore, we harness the knowledge from the pretrained discriminative model to prevent overfitting. In particular, to solve the entanglement problem between the correlated attributes from data and semantic latent correlation, we extend our model to jointly optimize multiple attributes and propose a novel decoupling loss and cross-assessment loss to disentangle them from both latent and image space. We further propose a novel-attribute disentanglement strategy to enable editing of novel attributes with unknown entanglements. Finally, we extend our model to accurately edit the fine-grained attributes. Qualitative and quantitative assessments demonstrate that our method outperforms state-of-the-art approaches across various datasets, including CelebA-HQ, RaFD, Danbooru2018 and LSUN Church.

Classifying Stand Compositions in Clover Grass Based on High-Resolution Multispectral UAV Images

In organic farming, clover is an important basis for green manure in crop rotation systems due to its nitrogen-fixing effect. However, clover is often sown in mixtures with grass to achieve a yield-increasing effect. In order to determine the quantity and distribution of clover and its influence on the subsequent crops, clover plants must be identified at the individual plant level and spatially differentiated from grass plants. In practice, this is usually done by visual estimation or extensive field sampling. High-resolution unmanned aerial vehicles (UAVs) offer a more efficient alternative. In the present study, clover and grass plants were classified based on spectral information from high-resolution UAV multispectral images and texture features using a random forest classifier. Three different timestamps were observed in order to depict the phenological development of clover and grass distributions. To reduce data redundancy and processing time, relevant texture features were selected based on a wrapper analysis and combined with the original bands. Including these texture features, a significant improvement in classification accuracy of up to 8% was achieved compared to a classification based on the original bands only. Depending on the phenological stage observed, this resulted in overall accuracies between 86% and 91%. Subsequently, high-resolution UAV imagery data allow for precise management recommendations for precision agriculture with site-specific fertilization measures.

Nitrogen-Doped 3D-Graphene Advances Near-Infrared Photodetector for Logic Circuits and Image Sensors Overcoming 2D Limitations.

The limitations of two-dimensional (2D) graphene in broadband photodetector are overcome by integrating nitrogen (N) doping into three-dimensional (3D) structures within silicon (Si) via plasma-assisted chemical vapor deposition (PACVD) technology. This contributes to the construction of vertical Schottky heterojunction broad-spectrum photodetectors and applications in logic devices and image sensors. The natural nanoscale resonant cavity structure of 3D-graphene enhances photon capture efficiency, thereby increasing photocarrier generation. N-doping can fine-tune the electronic structure, advancing the Schottky barrier height and reducing dark current. The as-fabricated photodetector exhibits exceptional self-driven photoresponse, especially at 1550 nm, with an excellent photoresponsivity (79.6 A/W), specific detectivity (1013 Jones), and rapid response of 130 μs. Moreover, it enables logic circuits, high-resolution pattern image recognition, and broadband spectra recording across the visible to near-infrared range (400-1550 nm). This research will provide new views and technical support for the development and widespread application of high-performance semiconductor-based graphene broadband detectors.

OsO2as the Contrast-Generating Chemical Species of Osmium-Stained Biological Tissues in Electron Microscopy.

Electron imaging of biological samples stained with heavy metals has enabled visualization of subcellular structures critical in chemical-, structural-, and neuro-biology. In particular, osmium tetroxide OsO4 has been widely adopted for selective lipid imaging. Despite the ubiquity of its use, the osmium speciation in lipid membranes and the process for contrast generation in electron microscopy (EM) have continued to be open questions, limiting efforts to improve staining protocols and therefore high-resolution nanoscale imaging of biological samples. Following our recent success using photoemission electron microscopy (PEEM) to image mouse brain tissues with synaptic resolution, we have used PEEM to determine the nanoscale electronic structure of Os-stained biological samples. Os(IV), in the form of OsO2, generates nanoaggregates in lipid membranes, leading to a strong spatial variation in the electronic structure and electron density of states. OsO2has a metallic electronic structure that drastically increases the electron density of states near the Fermi level. Depositing metallic OsO2 in lipid membranes allows for strongly enhanced EM signals and conductivity of biological materials. The identification of the chemical species and understanding of the membrane contrast mechanism of Os-stained biological specimens provides a new opportunity for the development of staining protocols for high-resolution, high-contrast EM imaging.

Current status of endoscopic ultrasound in the diagnosis of intraductal papillary mucinous neoplasms.

The new Kyoto guidelines for the management of intraductal papillary mucinous neoplasm (IPMN) provide evidence-based recommendations for the diagnosis and treatment of IPMN. Endoscopic ultrasonography (EUS) is a diagnostic modality with a high spatial resolution that allows detailed observation and obtaining cyst fluid or tissue samples via EUS-guided fine needle aspiration (EUS-FNA). Currently, EUS is an indispensable examination method for the diagnosis of pancreatic diseases. On the other hand, there have been concerns that EUS imaging tends to be highly operator-dependent, and may lack objectivity. Previous guidelines have assigned EUS as an option for patients with worrisome features. However, recent reports indicate that the sensitivity of EUS for the diagnosis of mural nodules (MNs) is more than 90%, comparable or superior to that of contrast-enhanced computed tomography or magnetic resonance cholangiopancreatography. The specific advantages of EUS in the diagnosis of IPMN are: (1) high spatial resolution imaging for the diagnosis of MNs, (2) contrast-enhanced EUS for differentiation of intra-cystic MNs from mucous clots, and (3) pathological diagnosis using EUS-FNA and differential diagnosis of a pancreatic cystic tumor by cystic fluid analysis. In order to utilize EUS in the diagnosis of IPMN, endoscopists are required to have the skills to provide sufficiently objective imaging findings.

Improved Operating Behavior of Self-Lubricating Rolling-Sliding Contacts under High Load with Oil-Impregnated Porous Sinter Material

Resource and energy efficiency are of high importance in gearbox applications. To reduce friction and wear, an external lubricant supply like dip or injection lubrication is used to lubricate tribosystems in machine elements. This leads to the need for large lubricant volumes and elaborate sealing requirements. One potential method of minimizing the amount of lubricant and simplifying sealing in gearboxes is the self-lubrication of tribosystems using oil-impregnation of porous materials. Although well established in low-loaded journal bearings, self-lubrication of rolling-sliding contacts in gears is poorly understood. This study presents the self-lubrication method using oil-impregnated porous sinter material variants. For this, the tribosystem of gear contacts is transferred to model contacts, which are analyzed for friction and temperature behavior using a twin-disk tribometer. High-resolution surface images are used to record the surface changes. The test results show a significant increase in self-lubrication functionality of tribosystems by oil-impregnated porous sinter material and a tribo-performance comparable to injection-lubricated tribosystems of a sinter material with additionally solid lubricant added to the sinter material powder before sintering. Furthermore, the analyses highlight a significant influence of the surface finish, and in particular the surface porosity, on the overall tribosystem behavior through significantly improved friction and wear behavior transferable to gear applications.

High-resolution diffusion magnetic resonance imaging and spatial-transcriptomic in developing mouse brain

Brain development is a highly complex process regulated by numerous genes at the molecular and cellular levels. Brain tissue exhibits serial microstructural changes during the development process. High-resolution diffusion magnetic resonance imaging (dMRI) affords a unique opportunity to probe these changes in the developing brain non-destructively. In this study, we acquired multi-shell dMRI datasets at 32 µm isotropic resolution to investigate the tissue microstructure alterations, which we believe to be the highest spatial resolution dMRI datasets obtained for postnatal mouse brains. We adapted the Allen Developing Mouse Brain Atlas (ADMBA) to integrate quantitative MRI metrics and spatial transcriptomics. Diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), and neurite orientation dispersion and density imaging (NODDI) metrics were used to quantify brain development at different postnatal days. We demonstrated that the differential evolutions of fiber orientation distributions contribute to the distinct development patterns in white matter (WM) and gray matter (GM). Furthermore, the genes enriched in the nervous system that regulate brain structure and function were expressed in spatial correlation with age-matched dMRI. This study is the first one providing high-resolution dMRI, including DTI, DKI, and NODDI models, to trace mouse brain microstructural changes in WM and GM during postnatal development. This study also highlighted the genotype-phenotype correlation of spatial transcriptomics and dMRI, which may improve our understanding of brain microstructure changes at the molecular level.

High Resolution TOF-MRA Using Compressed Sensing-based Deep Learning Image Reconstruction for the Visualization of Lenticulostriate Arteries: A Preliminary Study.

To investigate the visibility of the lenticulostriate arteries (LSAs) in time-of-flight (TOF)-MR angiography (MRA) using compressed sensing (CS)-based deep learning (DL) image reconstruction by comparing its image quality with that obtained by the conventional CS algorithm. Five healthy volunteers were included. High-resolution TOF-MRA images with the reduction (R)-factor of 1 were acquired as full-sampling data. Images with R-factors of 2, 4, and 6 were then reconstructed using CS-DL and conventional CS (the combination of CS and sensitivity conceding; CS-SENSE) reconstruction, respectively. In the quantitative assessment, the number of visible LSAs (identified by two radiologists), length of each depicted LSA (evaluated by one radiological technologist), and normalized mean squared error (NMSE) value were assessed. In the qualitative assessment, the overall image quality and the visibility of the peripheral LSA were visually evaluated by two radiologists. In the quantitative assessment of the DL-CS images, the number of visible LSAs was significantly higher than those obtained with CS-SENSE in the R-factors of 4 and 6 (Reader 1) and in the R-factor of 6 (Reader 2). The length of the depicted LSAs in the DL-CS images was significantly longer in the R-factor 6 compared to the CS-SENSE result. The NMSE value in CS-DL was significantly lower than in CS-SENSE for R-factors of 4 and 6. In the qualitative assessment of DL-CS images, the overall image quality was significantly higher than that obtained with CS-SENSE in the R-factors 4 and 6 (Reader 1) and in the R-factor 4 (Reader 2). The visibility of the peripheral LSA was significantly higher than that shown by CS-SENSE in all R-factors (Reader 1) and in the R-factors 2 and 4 (Reader 2). CS-DL reconstruction demonstrated preserved image quality for the depiction of LSAs compared to the conventional CS-SENSE when the R-factor is elevated.

Elucidating bacterial coaggregation through a physicochemical and imaging surface characterization

Bacterial coaggregation is a highly specific type of cell-cell interaction, well-documented among oral bacteria, and involves specific characteristics of the cell surface of the coaggregating strains. However, the understanding of the mechanisms promoting coaggregation in aquatic systems remains limited. This gap is critical to address, given the broad implications of coaggregation for multispecies biofilm formation, water quality, the performance of engineered systems, and diverse biotechnological applications. Therefore, this study aims to comprehensively characterize the cell surface of the coaggregating strain Delftia acidovorans 005P, isolated from drinking water, alongside a non-coaggregating strain, D. acidovorans 009P. By analyzing two strains of the same species, we aim to identify the factors contributing to the coaggregation ability of strain 005P. To achieve this, we employed a combination of physicochemical characterization, Fourier-transform infrared spectroscopy (FTIR), and advancing imaging techniques [transmission electron microscopy and cryo-electron tomography (cryo-ET)]. The coaggregating strain (005P) exhibited higher surface hydrophobicity, negative surface charge, and cell surface and co-adhesion energies than the non-coaggregating strain (009P). The chemical characterization of bacterial surfaces through FTIR revealed subtle differences, particularly in spectral regions linked to carbohydrates and phosphodiesters/amide III of proteins (860–930 cm−1 and 1212–1240 cm−1, respectively). Cryo-ET highlighted significant differences in pili structures between the strains, such as variations in length, frequency, and arrangement. The pili in the 005P strain, identified as pili-like adhesins, serve as key mediators of coaggregation. By integrating physicochemical analyses and high-resolution imaging techniques, this study conclusively links the coaggregation ability of D. acidovorans 005P to its unique pili characteristics, emphasizing their crucial role in microbial coaggregation in aquatic environments.

Collet-Sicard syndrome due to cervical artery dissection disclosed by high-resolution magnetic resonance imaging.

Cervical artery dissection (CAD) represents a leading cause of unilateral lower cranial nerve IX-XII palsy, known as Collet-Sicard syndrome (CSS). High-resolution magnetic resonance imaging (HR-MRI) is widely used in the evaluation of patients with CAD, providing information regarding vessel wall abnormalities and intraluminal thrombus. We present a patient with palsy of multiple lower cranial nerves in the context of CSS, attributed to unilateral spontaneous internal carotid artery dissection. We describe a 68-year-old man with unremarkable previous history, who presented with subacute, gradually worsening dysphagia and hoarse voice. Clinical examination revealed right-sided palsy of cranial nerves IX-XII. Three-dimensional fat-saturated black-blood T1-weighted high-resolution vessel wall imaging disclosed spontaneous dissection with intramural hematoma along the distal right internal carotid artery. Neck MRI showed inward displacement of right aryepiglottic fold, right pyriform sinus dilatation, and right true vocal cord in middle position, indicative of right vagus nerve palsy, atrophy of right trapezius and sternocleidomastoid muscles, due to right spinal accessory nerve palsy, and unilateral tongue atrophy with fatty infiltration, characteristic for right hypoglossal nerve palsy. This case highlights the utility of high-resolution vessel wall imaging and especially fat-saturated T1-weighted black-blood SPACE (sampling perfection with application-optimized contrast using different flip-angle evolutions) sequences in the accurate diagnosis of CAD, revealing the characteristic mural hematoma and intimal flap. HR-MRI is also valuable in the recognition of indirect signs of lower cranial nerve compression.

Absolute permeability estimation from microtomography rock images through deep learning super-resolution and adversarial fine tuning

The carbon capture and storage (CCS) process has become one of the main technologies used for mitigating greenhouse gas emissions. The success of CCS projects relies on accurate subsurface reservoir petrophysical characterization, enabling efficient storage and captured CO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{CO}_2$$\\end{document} containment. In digital rock physics, X-ray microtomography (μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu $$\\end{document}-CT) is applied to characterize reservoir rocks, allowing a more assertive analysis of physical properties such as porosity and permeability, enabling better simulations of porous media flow. Estimating petrophysical properties through numeric simulations usually requires high-resolution images, which are expensive and time-inefficient to obtain with μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu $$\\end{document}-CT. To address this, we propose using two deep learning models: a super-resolution model to enhance the quality of low-resolution images and a surrogate model that acts as a substitute for numerical simulations to estimate the petrophysical property of interest. A correction process inspired by generative adversarial network (GAN) adversarial training is applied. In this approach, the super-resolution model acts as a generator, creating high-resolution images, and the surrogate network acts as a discriminator. By adjusting the generator, images that correct the errors in the surrogate’s estimations are produced. The proposed method was applied to the DeePore dataset. The results shows the proposed approach improved permeability estimation overall.

Click3D: Click reaction across deep tissues for whole-organ 3D fluorescence imaging.

Click chemistry offers various applications through efficient bioorthogonal reactions. In bioimaging, pretargeting strategies have often been used, using click reactions between molecular probes with a click handle and reporter molecules that make them observable. Recent efforts have integrated tissue-clearing techniques with fluorescent labeling through click chemistry, allowing high-resolution three-dimensional fluorescence imaging. Nevertheless, these techniques have faced a challenge in limited staining depth, confining their use to imaging tissue sections or partial organs. In this study, we introduce Click3D, a method for thoroughly staining whole organs using click chemistry. We identified click reaction conditions that improve staining depth with our custom-developed assay. The Click3D protocol exhibits a greater staining depth compared to conventional methods. Using Click3D, we have successfully achieved whole-kidney imaging of nascent RNA and whole-tumor imaging of hypoxia. We have also accomplished whole-brain imaging of hypoxia by using the clickable hypoxia probe, which has a small size and, therefore, has high permeability to cross the blood-brain barrier.

Metasurface-integrated elliptically polarized laser-pumped SERF magnetometers

The emergence of biomagnetism imaging has led to the development of ultrasensitive and compact spin-exchange relaxation-free (SERF) atomic magnetometers that promise high-resolution magnetocardiography (MCG) and magnetoencephalography (MEG). However, conventional optical components are not compatible with nanofabrication processes that enable the integration of atomic magnetometers on chips, especially for elliptically polarized laser-pumped SERF magnetometers with bulky optical systems. In this study, an elliptical-polarization pumping beam (at 795 nm) is achieved through a single-piece metasurface, which results in an SERF magnetometer with a high sensitivity reaching 10.61 fT/Hz1/2 by utilizing a 87Rb vapor cell with a 3 mm inner diameter. To achieve the optimum theoretical polarization, our design combines a computer-assisted optimization algorithm with an emerging metasurface design process. The metasurface is fabricated with 550 nm thick silicon-rich silicon nitride on a 2 × 2 cm2 SiO2 substrate and features a 22.17° ellipticity angle (a deviation from the target polarization of less than 2%) and more than 80% transmittance. This study provides a feasible approach for on-chip polarization control of future all-integrated atomic magnetometers, which will further pave the way for high-resolution biomagnetism imaging and portable atomic sensing applications.

Scalable High-Resolution Single-Pixel Imaging via Pattern Reshaping

Scalable High-Resolution Single-Pixel Imaging via Pattern Reshaping

INNOVATIVE ANALYSIS OF MUD VOLCANO PATTERNS IN WESTERN AND SOUTHWESTERN PAKISTAN THROUGH SATELLITE IMAGERY

Mud volcanoes are geological features formed due to pressurized mud, water, gases accompanied by rocks that are expelled to the surface from underground. In Pakistan, there are more than 80 mud volcanoes. However, there is a lack of extensive, thorough mapping and analysis of Pakistan's mud volcanoes in the West and Southwest utilizing satellite image technologies. The present study aims to locate various clusters in South Western and Western regions of Pakistan, and to make a laboratory scale and conceptual model of mud volcanoes in this region to understand the underlying geological and tectonic processes. Firstly, we have used high-resolution satellite images to demarcate and identify various clusters and individual mud volcanoes. Secondly, mud volcanoes of a lesser-known western segment of Pakistan are the unexplored Pishin Basin (Qila Saifullah, Babu Cheena, Zhob). These mud volcanoes occur in distinct patterns which is the regional trend of Makran subduction zone and Pishin Basin. These mud volcanoes occur in clusters (Hingol National Park), and follow the regional structural trend of lineaments. Tracing these lineaments can help in the identification of more mud volcanoes. These active mud volcanoes release methane and hydrocarbons along with mud and water and can be identified by changes in the tone, and texture (Ormara) of the satellite image. However, few fossil or extinct mud volcanoes still have eroded (Gwadar) craters and are relatively diminished in size (~50m). They exhibit a wide variety of surface structures extruding gas and sometimes with bubbles having viscous hot fluid of mud. It could signify the presence of hydrocarbons beneath the surface. Chandragup mud volcano is an attractive place for Hindus, as they consider it a holy place. They are also important as a geoheritage and tourist sight and can generate significant revenue. Hingol National Park is a captivating site for those all over the world who adore to grasp nature thoroughly. These are significant as similar settings in other countries like Azerbaijan, Romania and New Zealand hold immense petroleum, geoheritage, geotourism and geothermal potential.

FORMICARY SWARM OPTIMIZED DEEP CNN FOR FACIAL EMOTION RECOGNITION FROM HUMAN FACIAL EXPRESSIONS

Facial emotion recognition (FER) is a dominant research area that captures the biological facial features and matches the facial data with existing databases to analyze the individual’s emotional state. Numerous techniques have been formulated for attaining effective FER. However, the occlusions, different head positions, deformed faces, motion blur under unrestricted settings, and complicated backgrounds make it complex to analyze the facial images. In this paper, the formicary swarm optimization-based deep convolutional neural network (FSO-opt DCNN) model is utilized for facial emotion detection for which JAFFE and the RAVDESS facial expression datasets are used. DCNNs are proficient with built-in feature extraction strategies from images to map the various facial expressions to the corresponding emotional states adopted for effective FER. In addition, the intensity, directional, and edge patterns as well as the correlation features extracted utilizing the hybrid textual pattern, RESNET 101 and VGG 16-based correlation modules assist the DCNN to attain the informative feature from the high-resolution images. Further, the formicary swarm optimization (FSO) is incorporated that effectively tunes the DCNN to capture the complex relationships between the learned features that excel the FER capability. Evaluating the metrics, the face recognition model using the RAVDESS dataset achieves notable efficiencies during a training percentage (TP) of 90%, with values of 97.51%, 95.48%, 99.55%, 95.48%, 97.48%, 96.47%, and a minimum loss of 2.49%. Simultaneously, the emotion detection model demonstrates robust efficiencies with metric values of 96.75%, 98.49%, 95.01%, 98.49%, 96.72%, 97.59%, and a minimum loss of 3.25%. Finally, the obtained results reveal the efficacy of the FSO-opt DCNN, particularly in face recognition tasks, as it outperforms the existing models across various datasets, showcasing its versatility and potential in facial analysis applications.