Facial Photographs Research Articles

An OpenCV and Single-Camera Facial Recognition Real-Time Attendance System

Abstract--Traditional attendance mechanisms have fallen short of providing an actual guarantee in attendance, especially in advanced commercial and academic milieus, based on the general practice of proxy attendance. This paper details the implementation of an attendance system based on technologies like cameras and facial recognition capabilities. By prohibiting unlawful attendance using proxy mechanisms, this system seeks to improve the accuracy of attendance tracking. We present a strong framework that takes, processes, and validates facial photos in real- time by utilising developments in computer vision and machine learning. This provides a creative way to get around the drawbacks of traditional attendance systems. Keywords—Facial recognition, Accuracy of attendance tracking, Computer vision, Machine learning, Real-time processing, Attendance validation, Advanced commercial and academic milieus.

Artificial intelligence model for diagnosis of coronary artery disease based on facial photos

Objective: To develop and validate an artificial intelligence (AI) diagnostic model for coronary artery disease based on facial photos. Methods: This study was a cross-sectional study. Patients who were scheduled to undergo coronary angiography (CAG) at Beijing Anzhen Hospital and Beijing Daxing Hospital from August 2022 to November 2023 were included consecutively. Before CAG, facial photos were collected (including four angles: frontal view, left and right 60° profile, and top of the head). Photo datasets were randomly divided into a training set, a validation set (70%), and a testing set (30%). The model was constructed using Masked Autoencoder (MAE) and Vision Transformer (ViT) architectures. Firstly, the model base was pre-training using 2 million facial photos obtained from the publicly available VGGFace dataset, and fine-tuned by the training and validation sets; the model was validated in the test set. In addition, the ResNet architecture was used to process the dataset, and its outputs were compared with those of the models based on MAE and ViT. In the test set, the area under the operating characteristic curve (AUC) of the AI model was calculated using CAG results as the gold standard. Results: A total of 5 974 participants aged 61 (54, 67) years were included, including 4 179 males (70.0%), with a total of 84 964 facial photos. There were 79 140 facial photos in the training and validation sets, with 3 822 patients with coronary artery disease; there were 5 824 facial photos in the test set, with 239 patients with coronary artery disease. The AUC value of the MAE and ViT model initialized with pre-training model weights was 0.841 and 0.824, respectively. The AUC of the ResNet model initialized with random weights was 0.810, while the AUC of the ResNet model initialized with pre-training model weights was 0.816. Conclusion: The AI model based on facial photos showes good diagnostic performance for coronary artery disease and holds promise for further application in early diagnosis.

Change and Detection of Emotions Expressed on People’s Faces in Photos

Human emotions are an element of attention in various areas of interest such as psychology, marketing, medicine, and public safety. Correctly detecting human emotions is a complex matter. The more complex and visually similar emotions are, the more difficult they become to distinguish. Making visual modifications to the faces of people in photos in a way that changes the perceived emotion while preserving the characteristic features of the original face is one of the areas of research in deepfake technologies. The aim of this article is to showcase the outcomes of computer simulation experiments that utilize artificial intelligence algorithms to change the emotions on people’s faces. In order to detect and change emotions, deep neural networks discussed further in this article were used.

Skin Markers of Premature Ageing in Patients with COPD: Results Form COSYCONET.

Background: Chronic obstructive pulmonary disease (COPD) is commonly associated with ageing, with the prevalence and severity increasing by age. Smoking-induced premature ageing is thought to contribute to COPD, particularly lung emphysema. This study aimed to explore the relationship between lung function impairment and skin texture, as a marker of biological or premature ageing, in COPD patients. Methods: A subcohort from the COSYCONET COPD-study was analyzed, where skin-relief replicas of the eye's outer corner and mid-lower inner arm were collected, along with semi-quantitative facial photographs. We examined the correlation between skin parameters and lung function, particularly the diffusing capacity (TLCO) as an indicator of emphysema. Results: Among 46 COPD patients (69 ± 8 years, 52% female), skin texture from the inner forearm, but not from the eye corner, was significantly associated with TLCO% predicted, with a higher skin roughness correlating with a lower TLCO (p = 0.015). This relationship persisted after adjusting for age, BMI, sex, pack years, and smoking status. No significant associations were found with facial photographs. Conclusions: These findings suggest that systemic ageing, reflected in inner arm skin texture, is linked to lung emphysema. Skin ageing markers may be valuable in future interventional studies involving anti-ageing treatments.

A Retrospective Pragmatic Two‐Center Clinical Study to Evaluate the Clinical Outcome of Triple‐Frequency Ultrasound in the Treatment of Mild‐to‐Severe Acne Vulgaris

ABSTRACTObjectivesEarlier, quickly alternating dual‐frequency ultrasound waves (LDM technology) were successfully applied for the treatment of different inflammatory skin conditions such as rosacea and acne. In this retrospective pragmatic two‐center clinical study, we applied the triple‐frequency LDM (TF‐LDM) technology with frequencies of 1/3/10 and 3/10/19 MHz for the treatment of mild‐to‐severe acne skin to assess the effectivity and sustainability of the treatment outcomes.MethodsTwenty‐two patients with mild‐to‐severe acne were included in this study: 11 patients were treated with TF‐LDM (1/3/10 MHz), and other 11 patients—with TF‐LDM (3/10/19 MHz). Assessment of the acne severity was done using the bilateral facial photographs. The photos were evaluated at baseline (T1), on the day of the last treatment (T2), and during the follow‐up controls (T3). Assessment of the acne severity was provided in accordance with a modified Global Evaluation Acne (mGEA) scale by nine independent dermatologists who were blinded to treatment assignments.ResultsThe average improvement of the mGEA scoring between T1 and T2 across all patients was 73.69% ± 13.90% (p < 0.01), whereas the skin improvement between T1 and T3 was 90.14% ± 8.35% (p < 0.01). The state of the skin was also statistically significantly improved between T2 and T3 (53.26% ± 29.24%, p < 0.02). There was no difference in treatment outcomes between the patients treated with TF‐LDM (1/3/10 MHz) and TF‐LDM (3/10/19 MHz).ConclusionsTF‐LDM is an effective method for the treatment of the mild‐to‐severe acne skin that provides a significant skin improvement and long‐lasting treatment results. The method demonstrates no significant side effects, is pain‐free, well tolerated, and highly accepted by patients.

Abstract 4136032: Digital Biomarkers Associated With Coronary Artery Calcium And Traditional Risk Factors Extracted From Facial Photos Through Multi-Label Deep Learning For Detecting Coronary Artery Disease

Background: Biomarkers like coronary artery calcium (CAC) and traditional risk factors are well-validated for coronary artery disease (CAD) assessment but not always available, and with limited workup efficiency and potential radiation risk. Facial features contain biological information related to atherosclerosis. Aims: We aim to extract CAC and traditional risk factor-associated digital biomarkers from facial images and evaluate their value in predicting CAD status compared with conventional clinical approaches. Methods: Suspected individuals referred for confirmatory CAD evaluation across nine centers were included. Participants in one center constituted the derivation set. External validation included one dataset of participants from a different time period in the derivation center and the other dataset from the other eight centers. We developed a multi-label deep-learning model to extract digital biomarkers from facial photos based on a multi-dimensional label of CAC score and eight traditional risk factors to comprehensively represent coronary atherosclerosis risk profile. The extracted digital biomarkers were evaluated for both effectiveness in reflecting components of the multi-dimensional label and clinical value in predicting obstructive CAD. Results: A total of 13248 facial photos from 3312 eligible participants (mean age, 58.5 years; 517 [25.9%] female) were included. In external validation, a set of digital biomarkers (FacialCAD) were extracted, effectively reflecting components of the multi-dimensional label, especially for CAC stratification (CAC>0, AUC 0.919 [0.885 – 0.949]; CAC≥100, AUC 0.906 [0.876 – 0.933]) and nearly perfect prediction for the age and sex. The performance of the FacialCAD in predicting obstructive CAD (AUC 0.721 [0.694–0.748]) significantly outperformed two guideline-recommended CAD models (0.721 vs. 0.653, P<0.001; 0.721 vs. 0.686, P=0.032), with further significant improvement when combined with these models (△AUC= +0.085, P<0.001; △AUC= +0.068, P<0.001). Conclusion: FacialCAD, a set of CAC and risk factors-associated novel digital biomarkers extracted from facial images, exhibited superior and incremental value to conventional clinical approaches in predicting CAD status.

Facial Image expression recognition and prediction system

Facial expression recognition system is an advanced technology that allows machines to recognize human emotions based on their facial expressions. In order to develop a robust prediction model, this research work proposes three distinct architectural models to produce a facial expression prediction system that looks like this: The first model is on using a support vector machine to carry out a classification task. As a follow-up to the second model, an attempt was made to create a Convolution Neural Network (CNN) using the VGG-NET (Visual Geometry Group Network). Following analysis of the results, an attempt was made to enhance the outcome using the third model, which used convolutional sequential layers linked to seven distinct expressions, and an inference was drawn based on loss and accuracy metric behavior. We will use a dataset of human picture facial images in this research, which has more than 35500 facial photographs and represents seven different types of facial expressions. We will analyze our data and make every effort to remove as much noise as we can before feeding that information to our model. We use the confusion matrix to assess the model’s performance after it has been implemented effectively. To demonstrate the effectiveness of our model architecture, we will generate bar graphs and scatter plots for each model to display model loss and accuracy. The output of this model is visualized with actual class and predictive class and the result has a graphical representation for each and every output facial Images which makes our recognition system user-friendly.

The Contribution of the Face-Name Associative Recognition Test to Objectifying the Impairment of Associative Memory in Subjective Cognitive Decline.

Subjective cognitive decline (SCD) is defined as a self-reported perception of cognitive decline that occurs without clear objective signs of cognitive impairment. There is still uncertainty in the literature about the reliability of SCD as an accurate indicator of the early stages of major neurocognitive disorders. Furthermore, objectifying cognitive impairment in SCD is difficult, mainly due to the insensitivity of the assessment instruments. The main objective of this study was to investigate the potential contribution of the face-name associative recognition test (FNART) to the objective identification of memory impairment in SCD. A research sample of 69 adults with SCD and 69 healthy controls (HCs) recruited in the community were administered in the FNART, which included 32 photographs of neutral faces associated with 32 first names. The total score of the HC group in the FNART was significantly better than that of the SCD group. Moreover, analyses based on the serial position of the stimuli showed that the SCD group performed significantly worse than the HC group only for the middle items (stimuli placed at the beginning or end of learning lists are more likely to be recalled than those presented in the middle), while no primacy and recency effects were found in the HCs. These findings indicate that associative episodic memory is more vulnerable in individuals with subjective cognitive decline (SCD) compared to those without cognitive complaints. Additionally, they suggest that the FNART may be effective in identifying cognitive decline in the preclinical stage of Alzheimer's disease.

Optimizing Attendance System: Integrating Liveness Detection and Deep Learning for Reliable Face Recognition

The study focuses on using vitality detection and deep learning technologies in the context of facial recognition in an IT presence management project. The combination of deep learning with vitality detection provides a considerable advancement in security and effectiveness. This work integrated vitality-detecting technology with in-depth learning in facial recognition systems. Vitality detection technologies are used to verify the authenticity of persons by examining live indicators such as movements or facial expressions before face recognition. Meanwhile, deep learning is used to analyze and process facial photos correctly by learning from large amounts of data and recognizing facial features in depth. The study data set consists of 1300 photographs of professional school instructors taken with official authority. Model testing and training are carried out in the Google Colab environment, using Python and the Hardy package. The test findings showed an 87% accuracy in face recognition, proving the system's capacity to consistently identify persons and distinguish real from false ones. Furthermore, the performance of Liveness Detection achieves 92% accuracy, as does the integration of Live Detection technology with Deep Learning at 78%.

Exploring the potential of historical images for the investigation of glacier changes: the case of Belvedere Glacier, Italian Alps

Historical images dispersed in various archives can be instrumental in documenting processes connected to glacier changes in mountains. A series of more than 29 engravings and photographs of the Monte Rosa east face have been found covering the period since 1789. Some images were produced by a combination of photography and engraving, a common approach before practical techniques for printing halftone images were introduced. Using a repeat photography approach, the present study documents the changes in the extent and debris cover of the Belvedere Glacier. Based on visual comparison of images, it appears that the debris cover of the lower part of the glacier developed mainly during the period of the 1860s–1880s. Furthermore, the image series documents the evolution of the terminus both in terms of elevation and shape, as well as a breach in lateral moraine which could be dated back to the end of the 19th century, well before the reported glacier lake outburst flood in 1904.

Dental photography using DSLR cameras vs smartphones

Background: Smartphone photography has increased in quality with the advancement of technology. Dental photography using a digital single-lens reflex (DSLR) camera has been a critical tool for patient education, treatment planning, self-evaluation, and research in the orthodontic profession. This study aims to determine if there is a significant difference in quality between facial and intraoral photographs taken with DSLR cameras and smartphones.Method: Dental students and practicing orthodontists took an 18-question survey to compare facial and intraoral photographs taken with a DSLR camera and a smartphone. Participants evaluated the photographs based on color, sharpness, depth of field, and overall quality. Chi-square analyses and Fisher's exact tests were used to evaluate potential differences across image sources and respondent types.Results: Three hundred twenty-nine participants completed this survey with an overall response rate of 74.9%. Facial photographs taken with a DSLR had significantly superior overall quality, sharpness, and depth of field compared to smartphone facial photographs (P<0.05). Intraoral photographs taken with a smartphone had significantly superior overall quality, color, sharpness, and depth of field compared to DSLR photographs (P< 0.01).Conclusions: This study suggests that intraoral photographs taken with a smartphone could provide a superior image quality compared to those taken by a DSLR camera, but not for facial photographs. As smartphone cameras continue to evolve, they have the potential to provide superior photographic quality for intraoral photographs compared to DSLR cameras.

Associations of Facial Shape With Physical Strength and 2D:4D in a Turkish Male and Female Sample.

Human sexual dimorphism in physical strength manifests itself in men having a greater muscle mass than women, reflecting ancestral roles in competition, protection, and provisioning. Prenatal testosterone exposure, approximated via the second-to-fourth digit ratio (2D:4D), is linked to increased muscular strength in both sexes, indicating a developmental influence. Previous research has shown that both physical strength and 2D:4D have facial shape correlates, especially in men, but most studies have focused on Western populations and one trait. We therefore hypothesized a broader relationship between facial shape and both physical strength and 2D:4D. In this study, we quantified the association between facial shape, handgrip strength (HGS), and 2D:4D in a non-Western Turkish sample (72 men, 55 women; Md = 22 y, SIR = 1.8 y) using two dimensional geometric morphometrics. Thirty-eight somatometric and 32 semi-landmarks were digitized on facial photographs taken in frontal view. Physical strength was assessed via handgrip strength (HGS), and the second digit length was divided by the fourth digit length to calculate 2D:4D. Both HGS and 2D:4D were significantly associated with shape in both sexes, but only in men did they explain a significant amount of facial variation. Thin-plates spline deformation grids and geometric morphometric morphs visualized the facial shape changes related to variations in handgrip strength, 2D:4D, and sexual dimorphism, enabling trait comparisons. This study contributes a comparative sample from the Middle East, which is indispensable to discern universalities from Western peculiarities. It provides evidence to better understand the biological basis of facial traits, which can potentially serve as increasingly relevant social cues in today's online and digital environments.

Effective encoder-decoder network for pupil light reflex segmentation in facial photographs of ptosis patients

Accurate segmentation of pupil light reflexes is essential for the reliable assessment of ptosis severity, a condition characterized by the drooping of the upper eyelid. This study introduces a novel encoder-decoder network specialized in reflex segmentation by focusing on addressing issues related to very small regions of interest from an architectural perspective. Specifically, the proposed network is designed to exploit low-level features effectively by integrating a multi-level skip connection and a 1 × 1 convolution-enhanced initial encoding stage. Assessed using a photograph image dataset from Chung-Ang University Hospital, which includes 87 healthy subjects, 64 with ptosis, and 257 with Graves’ orbitopathy (collected between January 2010 and February 2023), the proposed network outperforms five conventional encoder-decoders. Over 30 trials, the proposed network achieved a mean Dice coefficient of 0.767 and an Intersection over Union of 0.653, indicating a statistically significant improvement in the segmentation of reflex. Our findings show that an elaborate design based on the lowest-level skip connection and 1 × 1 convolution at initial stage enhances the segmentation of pupil light reflexes. The source code of the proposed network is available at https://github.com/tkdgur658/ReflexNet.

Leveraging openCV and image processing for crime identification through facial recognition

To maintain the safety and security of communities, law enforcement agencies provide vital functions such as crime prevention and identification. Identifying people with a criminal record efficiently and accurately is one of the biggest hurdles in this field. This study delves into a fresh strategy for taking on this problem by making use of state-of-the-art image processing methods and machine learning algorithms. The project's main goal is to identify convicted felons by utilizing OpenCV, a robust open-source computer vision toolkit, and different facial recognition algorithms. By giving them a tool that guarantees a faster reaction rate and improved accuracy in suspect identification, the system is aimed to boost the capacities of law enforcement organizations. This project is based on the tenet of criminal psychology, which states that criminals are prone to committing new crimes. The system's goal is to detect and track repeat criminals both before and after they commit crimes by making use of this information. The method works by taking pictures of people who are known to be criminals and putting them in a database. From there, they can be compared to either live video streams or static photographs taken by surveillance cameras. In order to analyze and identify facial characteristics effectively, algorithms like LBPH and the Haar cascade classifier are also used. The system's robustness and efficiency are guaranteed by integrating these approaches into the OpenCV framework. This research makes a valuable contribution to the field of crime prevention by offering law enforcement authorities a dependable and scalable alternative. This system provides a streamlined interface for storing, retrieving, and matching facial photos. It was built using Python 3.5 and uses SQLite to manage the database of offenders' details. Proactively monitoring persons with a criminal record is made easier and faster with the suggested method. Suspect identification is also made easier. This research showcases the possibility of integrating machine learning and image processing to enhance public safety and crime prevention initiatives. It is an early step towards more complete video surveillance systems.

Few-Shot Face Sketch-to-Photo Synthesis via Global-Local Asymmetric Image-to-Image Translation

Face sketch-to-photo synthesis is widely used in law enforcement and digital entertainment, which can be achieved by Image-to-Image (I2I) translation. Traditional I2I translation algorithms usually regard the bidirectional translation of two image domains as two symmetric processes, so the two translation networks adopt the same structure. However, due to the scarcity of face sketches and the abundance of face photos, the sketch-to-photo and photo-to-sketch processes are asymmetric. Considering this issue, we propose a few-shot face sketch-to-photo synthesis model based on asymmetric I2I translation, where the sketch-to-photo process uses a feature-embedded generating network, while the photo-to-sketch process uses a style transfer network. On this basis, a three-stage asymmetric training strategy with style transfer as the trigger is proposed to optimize the proposed model by utilizing the advantage that the style transfer network only needs few-shot face sketches for training. Additionally, we discover that stylistic differences between the global and local sketch faces lead to inconsistencies between the global and local sketch-to-photo processes. Thus, a dual branch of the global face and local face is adopted in the sketch-to-photo synthesis model to learn the specific transformation processes for global structure and local details. Finally, the high-quality synthetic face photo can be generated through the global-local face fusion sub-network. Extensive experimental results demonstrate that the proposed Global-Local Asymmetric (GLAS) I2I translation algorithm compared to SOTA methods, at least improves FSIM by 0.0126, and reduces LPIPS (alex), LPIPS (squeeze), and LPIPS (vgg) by 0.0610, 0.0883, and 0.0719, respectively.

Facial and dental arch shape in individuals with different bite force levels : A pilot study using geometric morphometrics.

This pilot study aimed to assess the relationship between bite force variation and dental arch and facial shape using geometric morphometrics, an advanced method of statistical analysis that provides adetailed shape analysis of astructure considering the spatial relationship of its parts. The sample consisted of 16German adult men and women. For each individual, maximum bite force was recorded in four positions: maximum intercuspation, protrusion, laterotrusion to the right and to the left. Facial and three-dimensional (3D) dental reconstructions were obtained from 3D facial photographs and 3D scans of dental stone models. Atotal of 51landmarks were placed. General shape variation was assessed by principal component analysis. Partial least squares analyses were performed to evaluate the covariation between bite force, facial shape, and dental shape. There was no clear pattern or statistically significant covariation between our variables. Our results suggest aweak relationship between bite force, dental arch, and facial shape. Considering previous work in this field, we propose that low masticatory loads, characteristic in Western urban populations, may explain this. Further studies should, therefore, address this issue, taking into account effect size, the mechanical properties of the diet, and other relevant variables.

Mapping perceived sentiments in university campuses with varied landscape metrics and climatic conditions.

A sustainable university campus should accommodate students to experience positive emotions, which can be evoked by sustainable landscape with green and blue spaces (GBS). This effect is location-dependent because local vegetative type is climate-determinative, but evidence is not sufficient for sentiments of people experiencing campus landscapes. Forty-seven university campuses were selected along a latitudinal gradient in mainland China, and 100 subjects were chosen per campus (50 indoor and 50 outdoor). Photos of the subjects' faces on Sina Weibo were collected. Facial expressions were assigned happy, sad, and neutral scores (n = 4,334). The average temperature (AveT) and blue space area (BlueA) showed negative relationships with latitude, thereby generating neutral emotion scores for subjects at indoor and outdoor locations. The ratio of green space area to host campus was the only landscape metric that depressed the presentation of happiness and enhanced sadness levels. Large water bodies should be built on campuses to induce calmness, and a high ratio of green spaces should be avoided. Mapping results show that campuses in eastern regions (Beijing and Liaoning) tend to elicit positive sentiments more frequently.

Photoanthropometric study: are non-professional photographs suitable for objective and reliable analysis of facial features?

The face has been widely investigated using professionally taken frontal and lateral photographs, however, there is a lack of studies of non-professional facial photographs. It is not known if they could be suitable for facial analysis. The analysis of non-professional photographs could allow the performance of cost- effective longitudinal studies. To determine if non-professional photographs could be used for a reliable analysis of facial features. The frontal profiles of 18-21-year-olds (35 males, 39 females) were measured by direct anthropometry, in addition, professional photographs were taken and non-professional photographs were obtained. Anthropometric landmarks were superimposed on those photographs. The indices calculated on the basis of the measurements of direct anthropometry and both types of photographs were compared. The comparison of the measurements of direct anthropometry and professional photographs showed no difference between 14 out of 25 male and 10 out of 25 female facial indices (p > 0.05) after comparing the results of direct anthropometry with those of non-professional photographs, no difference was found in 8 out of 25 male and 7 out of 25 female indices. These indices were mostly composed of vertical parameters and eye measurements. Vertical facial dimensions and eye measurements may not only be used interchangeably for both facial photographs and direct anthropometry, but may also be suitable for objective and reliable facial analyses.

Epigenomic and phenotypic characterization of DEGCAGS syndrome.

Developmental Delay with Gastrointestinal, Cardiovascular, Genitourinary, and Skeletal Abnormalities syndrome (DEGCAGS, MIM #619488) is caused by biallelic, loss-of-function (LoF) ZNF699 variants, and is characterized by variable neurodevelopmental disability, discordant organ anomalies among full siblings and infant mortality. ZNF699 encodes a KRAB zinc finger protein of unknown function. We aimed to investigate the genotype-phenotype spectrum of DEGCAGS and the possibility of a diagnostic DNA methylation episignature, to facilitate the diagnosis of a highly variable condition lacking pathognomonic clinical findings. We collected data on 30 affected individuals (12 new). GestaltMatcher analyzed fifty-three facial photographs from five individuals. In nine individuals, methylation profiling of blood-DNA was performed, and a classification model was constructed to differentiate DEGCAGS from controls. We expand the ZNF699-related molecular spectrum and show that biallelic, LoF, ZNF699 variants cause unique clinical findings with age-related presentation and a similar facial gestalt. We also identified a robust episignature for DEGCAGS syndrome. DEGCAGS syndrome is a clinically variable recessive syndrome even among siblings with a distinct methylation episignature which can be used as a screening, diagnostic and classification tool for ZNF699 variants. Analysis of differentially methylated regions suggested an effect on genes potentially implicated in the syndrome's pathogenesis.

Quantitative Evaluation of the Effects of Blepharoplasty on Visual Field, Corneal Changes, and Cosmetic Appearance.

Blepharoplasty is an important eyelid surgical procedure, involving aesthetic and functional changes to the eyelids. The aim of this study is to investigate the effects of upper eyelid blepharoplasty on changes in the visual field, corneal changes, and cosmetic appearance. This study was performed with preoperative and postoperative facial photographs of 32 patients who underwent bilateral blepharoplasty. Marginal reflex distance (MRD1), percentage of corneal exposure area (CEA), and brow height (BH) were obtained from photographs. intraocular pressure (IOP) and ocular biometric parameters were also obtained. The morphometric and ocular parameters obtained from preoperative, postoperative first-month, and postoperative third-month patients were compared using repeated measures ANOVA test. Significant differences in MRD1, CEA, and BH were observed between preoperative, postoperative first-month, and postoperative third-month patients. Improvements were observed in left axis and right corneal astigmatism axis measurements in the postoperative third month compared with preoperative measurements, and the positive change was statistically significant (P=0.020, P=0.036). Upper eyelid blepharoplasty could provide satisfactory functional and aesthetic outcomes by contributing to the improvement of pseudoptosis and reduction in eyebrow height postoperatively.