Frontal View Research Articles

AI-based dental caries and tooth number detection in intraoral photos: Model development and performance evaluation

ObjectivesIn this study, we aimed to integrate tooth number recognition and caries detection in full intraoral photographic images using a cascade region-based deep convolutional neural network (R-CNN) model to facilitate the practical application of artificial intelligence (AI)-driven automatic caries detection in clinical practice. MethodsOur dataset comprised 24,578 images, encompassing 4787 upper occlusal, 4347 lower occlusal, 5230 right lateral, 5010 left lateral, and 5204 frontal views. In each intraoral image, tooth numbers and, when present, dental caries, including their location and stage, were annotated using bounding boxes. A cascade R-CNN model was used for dental caries detection and tooth number recognition within intraoral images. ResultsFor tooth number recognition, the model achieved an average mean average precision (mAP) score of 0.880. In the task of dental caries detection, the model's average mAP score was 0.769, with individual scores spanning from 0.695 to 0.893. ConclusionsThe primary objective of integrating tooth number recognition and caries detection within full intraoral photographic images has been achieved by our deep learning model. The model's training on comprehensive intraoral datasets has demonstrated its potential for seamless clinical application. Clinical SignificanceThis research holds clinical significance by achieving AI-driven automatic integration of tooth number recognition and caries detection in full intraoral images where multiple teeth are visible. It has the potential to promote the practical application of AI in real-life and clinical settings.

Influence of orthodontic bracket prescription on smile attractiveness in patients with different sagittal skeletal patterns: A 3-dimensional cross-sectional study

The objective of this study was to evaluate the influence of orthodontic bracket prescription on smile attractiveness. Three women were chosen according to their sagittal skeletal pattern: skeletal Class I, II, or III malocclusion. For each, 3 smiling pictures were taken in frontal, oblique, and lateral views. The maxillary arch was then scanned, and a 3-dimensional digital model was constructed on the OrthoAnalyzer software. The information of 3 orthodontic prescriptions, namely Roth, MBT, and Ricketts, was simulated after the virtual placement of orthodontic brackets on 10 maxillary teeth. The simulations were then superimposed on the smile photographs in the 3 views, creating a total of 27 images. Groups of orthodontists, dentists, and laypeople rated the attractiveness of each smile using the visual analog scale. This study included 167 evaluators: 54 orthodontists, 54 dentists, and 59 laypersons. In terms of orthodontic prescription, MBT esthetic scores were 4% lower than Roth scores (P<0.001), and Ricketts scores were 13.6% lower than Roth scores (P<0.001). In terms of skeletal pattern, Class II and III scores were 15.0% and 16.4% lower than Class I scores, respectively (P<0.001). Regarding the smiling view, oblique view scores were 3.1% lower than frontal view scores (P= 0.031), whereas lateral view scores were 8.4% lower than frontal view scores (P<0.001). In contrast to gender and age, the specialty of the evaluators significantly affected the esthetic rating of the smiles. The attractiveness of a smile is affected by the prescription of the brackets. The Roth prescription tends to obtain the most favorable esthetic scores, especially in sagittal smiling images. Profile smiles with excessively proclined incisors are considered unattractive. Different views of the same smile do not get similar esthetic scores because sagittal views are rated the most severely.

People Tracking in Video Surveillance Systems Based on Artificial Intelligence

As security is one of the basic human needs, we need security systems that can prevent crimes from happening. In general, surveillance videos are used to observe the environment and human behavior in a given location. However, surveillance videos can only be used to record images or videos, without additional information. Therefore, more advanced cameras to obtain other additional information such as the position and movement of people. This research extracted this information from surveillance video footage using a person tracking, detection and identification algorithm. The framework for these is based on deep learning algorithms, a popular branch of artificial intelligence. In the field of video surveillance, a person tracking is considered a challenging task. Many computer vision, machine learning and deep learning techniques have been developed in recent years. The majority of these techniques are based on frontal view images or video sequences. In this work, we will compare some previous work related to the same topic.

Facial Analysis of the Igbo Ethnic Group of Nigeria for the Evaluation of Sexual Dimorphism

Background: Facial analysis defines the measurement of soft tissue in the face used in forensics, anthropology, artistic design, and facial reconstruction. The study aims to evaluate the sexual differences in facial parameters among the Igbo ethnic group of Nigeria.&#x0D; Methods: 400 (200 males and 200 females) subjects of Igbo origin volunteered for the study. Each subject's frontal view photographs were taken in the Natural Head Position (NHP) and analyzed using Pro-Image Facial Software Version 2 to evaluate the nasal width, nasal height, mouth width, facial width, facial height, inner canthal distance, and outer canthal distance. Data obtained were analyzed with IBM SPSS (version 23) and independent T-test as an inferential statistic.&#x0D; Results: The result showed that NW was (M=5.16±0.65, F=5.34±0.53, t=-2.91, p=0.00), NH was (M=5.61±0.62, F=5.26±0.57, t=5.92, p=0.00), MW was (M=6.64±0.7, F=6.54±0.71, t=1.46, p=0.14), FW was (M=16.17±1.31, F=15.07±2.04, t=6.39, p=0.00), FH was (M=16.63±1.11, F=16.08±1.31, t=4.36, p=0.00), En was (M=4.62±0.46, F=4.31±0.47, t=6.75, p=0.00) and Ex was (M=12.23±0.86, F=12.16±1.01, t=0.82, p=0.41).&#x0D; Conclusion: The nasal width and Height, facial width and height, and inner canthal distance show gender differences among the Igbo ethnic group of Nigeria. This study will have a practical application in Forensics, Anthropology, Artistic and Industrial design, and to medical professionals involved in facial reconstruction.

Experimental investigation on low-velocity impact behavior of glass, Kevlar, and hybrid composites with an elastomeric polyurethane matrix

Low-velocity impacts represent a critical dynamic condition for engineering structures. Combining two reinforcing fibers in a single matrix, i.e., hybridization, is considered a feasible way to improve composite performance. In this context, this paper presents an experimental work on composites with Kevlar and glass fabrics and a novel thermoset polyurethane matrix. The coupons are manufactured by vacuum infusion technique and low-velocity impact tests are carried out. First, the impact behavior of Kevlar and glass laminates of different thicknesses is assessed, and then impact tests are performed on different configurations of hybrid laminates, both symmetric and non-symmetric. For the non-symmetric specimens, impact tests were conducted on both sides of the stack. Load vs displacement curves are reported along with absorbed energy. To investigate the damage mechanism, the front, back, and cross-section views of the specimens are analyzed, and features related to the stacking sequences are discussed. Thermographic analyses are carried out on the impacted specimens to further analyze damage. The failure mechanisms are different from traditional epoxy composites and a hybridization effect is reported. The results evidence that the hybrid coupons are viable for structural applications, being capable of absorbing high-impact energies, in particular, non-symmetric hybrid laminates outperformed the Kevlar, glass, and symmetric ones, absorbing roughly 15% less energy for the highest energy impact.

Visual analysis of fatigue in Industry 4.0

The performance of manufacturing operations relies heavily on the operators’ performance. When operators begin to exhibit signs of fatigue, both their individual performance and the overall performance of the manufacturing plant tend to decline. This research presents a methodology for analyzing fatigue in assembly operations, considering indicators such as the EAR (Eye Aspect Ratio) indicator, operator pose, and elapsed operating time. To facilitate the analysis, a dataset of assembly operations was generated and recorded from three different perspectives: frontal, lateral, and top views. The top view enables the analysis of the operator’s face and posture to identify hand positions. By labeling the actions in our dataset, we train a deep learning system to recognize the sequence of operator actions required to complete the operation. Additionally, we propose a model for determining the level of fatigue by processing multimodal information acquired from various sources, including eye blink rate, operator pose, and task duration during assembly operations.

A Multi-Angle Appearance-Based Approach for Vehicle Type and Brand Recognition Utilizing Faster Regional Convolution Neural Networks

Vehicle type and brand information constitute a crucial element in intelligent transportation systems (ITSs). While numerous appearance-based classification methods have studied frontal view images of vehicles, the challenge of multi-pose and multi-angle vehicle distribution has largely been overlooked. This paper proposes an appearance-based classification approach for multi-angle vehicle information recognition, addressing the aforementioned issues. By utilizing faster regional convolution neural networks, this method automatically captures crucial features for vehicle type and brand identification, departing from traditional handcrafted feature extraction techniques. To extract rich and discriminative vehicle information, ZFNet and VGG16 are employed. Vehicle feature maps are then imported into the region proposal network and classification location refinement network, with the former generating candidate regions potentially containing vehicle targets on the feature map. Subsequently, the latter network refines vehicle locations and classifies vehicle types. Additionally, a comprehensive vehicle dataset, Car5_48, is constructed to evaluate the performance of the proposed method, encompassing multi-angle images across five vehicle types and 48 vehicle brands. The experimental results on this public dataset demonstrate the effectiveness of the proposed approach in accurately classifying vehicle types and brands.

Instance Segmentation Evaluation For Traffic Signs

This research paper focuses on developing a traffic sign recognition system based on the You Only Look At Coefficients (YOLACT) model, a one-stage instance segmentation model that offers high performance in terms of accuracy and reliability. However, the performance of YOLACT is influenced by various conditions such as day/night and different angles of objects. Therefore, this study aims to evaluate the impact of different angles and environments on the performance of the system. The paper discusses the framework, backbone structure, prototype generation branch, mask coefficient, and mask assembly used in the system. ResNet-101 and ResNet-50 were used as the backbone structure to extract feature maps of objects in the input image. The prototype generation branch generates prototype masks using fully convolutional networks (FCN), and the mask coefficient branch generates the Mask assembly using the sigmoid nonlinearity. Two models, YOLACT and Mask-RCNN, were evaluated in terms of mean precision (mAP) and frames per second (FPS) with the front view dataset. The results show that YOLACT outperforms Mask-RCNN in terms of accuracy and speed. For an image resolution of 550x550, YOLACT with Resnet-101 was considered the best model in this article since it achieves over 80% precision, recall, specificity, and accuracy in various conditions such as day, night, left and right, and forward-looking angles.

A new freshwater Ceratomyxa species (Myxozoa: Ceratomyxidae) parasitizing a sciaenid fish from the Amazon Basin, Brazil.

Myxozoans of Ceratomyxidae Doflein, 1899 are common coelozoic parasites of marine life, and are also found less frequently in freshwater fish. The present study describes Ceratomyxa ranunculiformis n. sp. as a new freshwater myxosporean species infecting the gall bladder of the Amazonian sciaenid Plagioscion squamosissimus. The new Ceratomyxa was described based on its host, myxospore morphology, ribosomal rDNA gene sequencing, parasite distribution, and phylogenetic analysis. Immature and mature plasmodia were tadpole-shaped or pyriform, and exhibited slow undulatory motility. The myxospores were elongated and crescent-shaped in the frontal view, with a sutural line between two valves, which had rounded ends. The measurements of the formalin-fixed myxospores were: average length 4.9 (4.0-6.6) μm, average thickness 37.6 (32.4-43.9) μm, average posterior angle 165° (154°-173°). Two ovoid polar capsules of equal size, average length 2.0 (1.4-3.0) μm and average width 1.9 (1.4-2.4) μm, were located adjacent to the suture and contained polar filaments with 2-3 coils. The integrated comparative analysis of the morphological characteristics and molecular analyses of the ribosomal rDNA genes supported the identification of a new species of coelozoic Ceratomyxa. Maximum likelihood analyses showed the new species clustering within a well-supported clade, together with all the other Amazonian freshwater ceratomyxids.

Variation in flower size and shape of Impatiens capensis is correlated with urbanization in Montreal, Canada.

Urbanization is changing the conditions in which many species live, forcing them to adjust to these novel environments. Floral size and shape are critical traits for the reproduction of plants pollinated by animals as they are involved in the attraction of pollinators and in efficient pollination. Variation in size and shape could be affected by urbanization via its modification of the abiotic environment (habitat fragmentation, water availability, temperature, soil properties), or via its impact on the biotic environment of plants (pollination, herbivory). Although numerous studies have assessed the impact of urbanization on pollinator communities and many plant traits, few have investigated its impact on floral size and shape while quantifying the proportion of the total urbanization effect that is due to biotic interactions. In this study, we tested if urbanization and pollinator visitation rates affect the flower shape of the spotted jewelweed, Impatiens capensis. We quantified the size and shape of flowers in frontal and profile views using geometric morphometrics for 228 individuals from six populations from the region of Montreal, Canada. Pollinator visitation rates were estimated at each site and the main pollinators were found to be bumblebees, honeybees and hummingbirds. We found that floral size and shape are significantly correlated with urbanization as measured by the amount of vegetation in the surrounding environment of the plants (mean normalized vegetation index, NDVI) and by the visitation rates of bumblebees and honey bees. Partitioning of the total flower shape variation suggests that urbanization affects flower shape through abiotic factors and via its impact on pollinator visitation rates. While further studies from other cities are necessary to confirm the role of urbanization in shaping the floral shape of I. capensis, these results support the idea that urbanization could affect flower shapes.

MMAF-Net: Multi-view multi-stage adaptive fusion for multi-sensor 3D object detection

In this paper, we propose a 3D object detection method called MMAF-Net that is based on the multi-view and multi-stage adaptive fusion of RGB images and LiDAR point cloud data. This is an end-to-end architecture, which combines the characteristics of RGB images, the front view of point clouds based on reflection intensity, and the bird’s eye view of point clouds. It also adopts a multi-stage fusion approach of “data-level fusion + feature-level fusion” to fully exploit the strength of multimodal information. Our proposed method addresses key challenges found in current 3D object detection methods for autonomous driving, including insufficient feature extraction from multimodal data, rudimentary fusion techniques, and sensitivity to distance and occlusion. To ensure the comprehensive integration of multimodal information, we present a series of targeted fusion methods. Firstly, we propose a novel input form that encodes dense point cloud reflectivity information into the image to enhance its representational power. Secondly, we design the Region Attention Adaptive Fusion module utilizing an attention mechanism to guide the network in adaptively adjusting the importance of different features. Finally, we extend the 2D DIOU (Distance Intersection over Union) loss function to 3D and develop a joint regression loss based on 3D_DIOU and SmoothL1 to optimize the similarity between detected and ground truth boxes. The experimental results on the KITTI dataset demonstrate that MMAF-Net effectively addresses the challenges posed by highly obscured or crowded scenes while maintaining real-time performance and improving the detection accuracy of smaller and more difficult objects that are occluded at far distances.

SMIFormer: Learning Spatial Feature Representation for 3D Object Detection from 4D Imaging Radar via Multi-View Interactive Transformers

4D millimeter wave (mmWave) imaging radar is a new type of vehicle sensor technology that is critical to autonomous driving systems due to its lower cost and robustness in complex weather. However, the sparseness and noise of point clouds are still the main problems restricting the practical application of 4D imaging radar. In this paper, we introduce SMIFormer, a multi-view feature fusion network framework based on 4D radar single-modal input. SMIFormer decouples the 3D point cloud scene into 3 independent but interrelated perspectives, including bird’s-eye view (BEV), front view (FV), and side view (SV), thereby better modeling the entire 3D scene and overcoming the shortcomings of insufficient feature representation capabilities under single-view built from extremely sparse point clouds. For multi-view features, we proposed multi-view feature interaction (MVI) to exploit the inner relationship between different views by integrating features from intra-view interaction and cross-view interaction. We evaluated the proposed SMIFormer on the View-of-Delft (VoD) dataset. The mAP of our method reached 48.77 and 71.13 in the fully annotated area and the driving corridor area, respectively. This shows that 4D radar has great development potential in the field of 3D object detection.

A New Algorithm for Secondary Repair of Unilateral Cleft Lip Nasal Deformity.

Following primary surgery for unilateral cleft lip palate (UCLP), cleft lip nasal deformities (CLNDs) (nasal asymmetry, collapsed nasal alae, and a widened alar base) are generally inevitable and often require secondary rhinoplasty. However, reconstructing a cleft nose with an alar tissue deficiency remains challenging for rhinoplasty surgeons. The manifestations of common deformities are described herein, and a secondary rhinoplasty technique for unilateral CLNDs using a nasolabial flap (NLF) has been proposed for patients with alar tissue deficiency. Secondary rhinoplasties were performed in 12 patients with unilateral CLNDs between 2020 and 2021 using a NLF. Photogrammetric measurements were performed preoperatively and postoperatively. A total of 12 flaps were successfully transferred. Ten patients were followed up for >1 year. Significant postoperative decreases in nasal alar width were measured in both the base view (p < 0.050) and the frontal view (p < 0.050). Despite the additional facial scars that occurred in some cases, all patients were satisfied with the aesthetic effects. The NLF achieved satisfactory results in secondary rhinoplasty of unilateral CLND for patients with nasal tissue deficiencies in whom the surgeon weighed the potential benefits over postoperative scarring. 4 Laryngoscope, 134:1648-1655, 2024.

Myxobolus nekrasovae n. sp. (Cnidaria, Myxozoa) is a new species parasitizing the gills of the gibel carp, Carassius auratus gibelio

A new Myxobolus species, Myxobolus nekrasovi n. sp., was found in the gill arch of the gibel carp Carassius auratus gibelio during investigation of fish myxosporean fauna of ponds of Lake Baikal basin. The parasites were studied on the basis of spore morphology, as well as with histological and molecular methods. Mature spores of M. nekrasovi n. sp. are ellipsoidal in frontal view and lemon-shaped in lateral view, measuring 13.84 ± 0.4 (12.2–15) μm in length, 9.73 ± 0.2 (8.5–10.7) μm in width, 6,75 ± 0.1 (6.0–7.6) μm in thickness. Polar capsules are unequal and pyriform, measuring: length 6.31 ± 0.1 (5.4–7.4), width 3.49 ± 0.04 (3.12–4) μm and length 2.88 ± 0.1 (2.1–3.5), width 1.4 ± 0.03 (1–1.6) μm. Phylogenetic analysis with the SSU rDNA gene shows Myxobolus nekrasovae n. sp. as a sister species of the subclade formed by Thellohanellus sinensis, Myxobolus acutus, M. zhaltsanovae that infect gibel carp Carassius auratus gibelio.

Comparative Evaluation of Golden Proportion and Recurring Esthetic Dental (RED) Proportion in Natural Dental Esthetics

This study explores the differences between the distribution of Golden Proportion and Recurring Esthetic Dental (RED) Proportion in natural maxillary anterior teeth. Photographs of the subject’s anterior teeth were taken from the frontal view. The width ratios of canine to lateral incisor and lateral incisor to central incisor were calculated. The existence of the Golden Proportion was considered when the width ratios between canine to lateral incisor or lateral incisor to central incisor were within the range of 0.55–0.66. To evaluate the existence of RED proportion in each subject, the width ratio of canine to lateral incisor was compared with the width ratio of lateral incisor to central incisor. The distribution of the Golden proportion was observed in 66.7% and the RED proportion was observed in 22.2% of the natural dentition of the study population. The existence of the Golden proportion was significantly higher than the existence of the RED proportion in well-aligned natural dentition in the study population (P &lt; 0.05). Clinicians may choose the Golden proportion over the RED proportion during the maxillary anterior esthetic treatment procedure.

Classifications of Prominent Mandibular Angle and Surgical Considerations for Mandibuloplasty.

Contouring surgery is an operation that makes a smooth, oval-shaped face by removing protruding parts of the face. In particular, mandibuloplasty is a procedure that softens the shape of the mandible and is a popular procedure among Asians with a dominant mesocephalic profile. In this study, we tried to discuss the considerations before surgery by dividing the mandibular shape into morphologic aspects in the lateral and frontal views and anatomic considerations. In the lateral view, it is essential to consider the posterior ramal height and mandibular body length of each patient. These factors are closely related to the postoperative outcomes. Consideration of the mandibular plane angle is also essential. In the frontal view, the position of the gonial angle relative to the ramal body should be considered because the position of the gonial angle can affect the difficulty of operation. Anatomic considerations include antero-posterior ramal width, bucco-lingual width of mandibular body, amount of mouth opening, and location of inferior alveolar nerve. These are important factors that determine the difficulty of the operation. In this study, classification according to the shape and shape of the mandible was presented, and considerations for each classification were discussed. Cosmetic surgery is a socio-psychologically meaningful procedure that not only improves the esthetics but also improves the mental well-being of the patients. Therefore, it will be possible to provide better medical services to patients if clinicians are fully aware of the factors that can increase the difficulty of the operation and the factors that affect the surgical outcome.

Clubfoot Deformity Treatment with Ilizarov Apparatus in the Paediatric Population without Corrective Osteotomies and Soft Tissue Release: A Cross-Sectional Study.

Neglected Club Foot deformity is not an uncommon limb anomaly encountered by orthopaedic surgeons. Many treatment methods have been proposed. Ilizarov apparatus is one of the techniques used to correct this deformity. In this cross-sectional study 47 patients (56 feet) between the ages of 5 and 10 years with clubfoot deformity were treated using the Ilizarov external fixator. Age, sex, type of deformity, and radiographic parameters were measured on foot radiographs. Also, the American Orthopaedic Foot and Ankle Society (AOFAS) score and the Dimeglio classification were recorded for each patient before and after treatment. The treatment was unilateral in 38 patients and bilateral in 9 patients. 39 patients (69.6%) were male, and 17 patients (30.4%) were female with a mean age of 7.86 ± 1.4 years. Plantar angles of ankle flexion and ankle flexion curve increased from 20.12±6.52 and -16.51±8.36 to 25.89±6.44 and 6.19±6.42, respectively. There was also an improvement in the talocalcaneal and tibiocalcaneal angles. Also, the angle between the first metatarsus and the talus in the front and side views improved (P<0.00). Additionally, the mean AOFAS score and Dimeglio classification significantly improved. Three cases were complicated with distal tibial physeal separation that were treated with additional open surgeries. Ilizarov technique without osteotomies and soft tissue release could be considered a less invasive and successful method of treatment for neglected clubfoot deformity in patient five to ten years old that are not good candidate for Ponseti method.

Applying the MD Codes™ to Treat Emotional and Social Attributes with HA Fillers: A Retrospective Serial Case Study.

Aesthetic treatment directed at improving facial emotional messages and social attributes may maximize patient satisfaction with treatment results. The MD Codes™ is an approach to facial aesthetic treatment that accounts for emotional messages and social attributes while minimizing variability in patient assessment, injection technique, injection sites, and treatment outcomes. To assess the impact of the MD Codes systematic treatment approach on emotional and social attributes and natural appearance after facial aesthetic treatment with hyaluronic acid (HA) fillers. A retrospective analysis was performed in patients treated with 12 mL of HA fillers (Juvéderm® Vycross™ products) per patient using the MD Codes. Study injectors, experienced clinician observers, clinical staff, laypeople, and patients evaluated impact of treatment on emotional and social attributes and natural appearance based on pre- and post-treatment photographs of patients. Twelve patients and 49 external observers, from 12 countries, evaluated the images. More than 90% of patients named "eye bags" as the facial area most in need of aesthetic treatment; >55% of external observers identified "cheeks." The top 3 facial emotional attributes noted by all evaluators after treatment were, in order, "less tired", "less saggy", and "younger." All patients reported a visible and positive change in emotional attributes except for "less sad." After 9 months, most patients reported looking "less tired" (n=11/12), "less saggy" (n=8/12), and "less sad" (n=6/12). At months 6 and 9, all patients rated their appearance as natural looking. At least 90% of external observers considered the frontal view results natural for all but 1 patient. Patients reported improved positive social attributes (eg, "looking friendlier") and improvements in well-being after treatment. No serious side effects were reported. Implementing the MD Codes for facial aesthetic treatment may improve facial emotional attributes as well as enhance social attributes and well-being, with natural-looking results.

DETERMINANTS OF THE OPTICAL ILLUSIONS EFFECT ON THE DALEM AGENG PRABAYEKSA ROOF, DALEM MANGKUBUMEN AREA, YOGYAKARTA

Javanese buildings can be understood both physically and non-physically, one of them is the Dalem Ageng Prabayeksa which is in the Dalem Mangkubumen complex. This building has the most important and sacred position in the complex with the uniqueness of the hip roof (limasan), which cannot be found in other hip roof buildings. This study focuses on optical illusions that occur on the roof of the building in front (south) and rear (north) views using explorative descriptive methods. Optical illusions that affect visual perception occur because of the imaginary perspective lines formed from the diagonals of the roof of the building so that an illusion is created that make the roof of the building look wider and taller than it should be. The roof of the building is also formed from proportions that are in accordance with the reference for the building as a house and the building as a place of worship (temples) which supports how optical illusions can occur so that a visual perspective and meaning behind the design of the roof appears which makes sense and is in line with the position of the Dalem Ageng Prabayeksa building as the most important and sacred building in the Dalem Mangkubumen complex.

Generative Adversarial Network Based Face Frontalization: A Review

Abstract: Face frontalization can boost the performance of face recognition methods and has made significant progress with the development of Generative Adversarial Networks (GANs). Face frontalization, the process of synthesizing frontal views of faces from arbitrary poses, plays a pivotal role in numerous computer vision applications including facial recognition, emotion analysis, and virtual reality. In this study, we propose a novel approach leveraging Generative Adversarial Networks (GANs) to address the challenging task of face frontalization. Our model integrates a carefully designed architecture capable of disentangling pose variations from facial features. Additionally, a multi-stage training strategy is employed to enhance the network's capacity for learning complex pose-to-pose mappings