Facial Features Research Articles

Face Recognition Using LBPH and CNN

Objective:: The purpose of this paper was to use Machine Learning (ML) techniques to extract facial features from images. Accurate face detection and recognition has long been a problem in computer vision. According to a recent study, Local Binary Pattern (LBP) is a superior facial descriptor for face recognition. A person's face may make their identity, feelings, and ideas more obvious. In the modern world, everyone wants to feel secure from unauthorized authentication. Face detection and recognition help increase security; however, the most difficult challenge is to accurately recognise faces without creating any false identities. Methods:: The proposed method uses a Local Binary Pattern Histogram (LBPH) and Convolution Neural Network (CNN) to preprocess face images with equalized histograms. Results:: LBPH in the proposed technique is used to extract and join the histogram values into a single vector. The technique has been found to result in a reduction in training loss and an increase in validation accuracy of over 96.5%. Prior algorithms have been reported with lower accuracy when compared to LBPH using CNN. Conclusion:: This study demonstrates how studying characteristics produces more precise results, as the number of epochs increases. By comparing facial similarities, the vector has generated the best result.

A birchbark anthropomorphic article from the Early Iron Age Novotroitskoye necropolis in the Upper Ob

This paper discusses a burial doll discovered within the Early Iron Age necropolis of Novotriotskoye-1 during field research by the expedition of Barnaul State Pedagogical Institute under the direction of A.P. Umanskii in the Talmensky District of Altai Krai. The object found in a woman's burial is an anthropomorphic image with wing-shaped arms and schematically indicated facial features, namely prominent eyes, eyebrows, nose and gouged mouth. The purpose of this work is to publish the Novotriotskoye idol in high quality, since in various publications by A.P. Umanskii and co-authors it was presented only in schematic drawings. Cleaning the object from field dirt, undertaken by the authors, made it possible to better observe the stylistic features of the artifact. Furthermore, the authors made an attempt to create experimental stylistic replicas of the analysed article. Three scenarios of its production have been implemented, and the details are presented in this publication. The authors suggest that the schematic exterior of the Novotriotskoye object allows considering it as a peculiar burial doll. The iconography of the Novotriotskoye anthropomorphic image has been characterized in the context of connections of the Upper Ob River region population of the second half of the 1st mil. AD with the cultures of Northern Asia (Tashtyk, Kokel, “Bulan-Koby”). A wide range of archaeological and ethnographic parallels to the discussed anthropomorphic ob-ject has been analyzed. The authors conclude that the routine production of anthropomorphic images and masks was deeply rooted in the Ob River region, and it appeared in the Early Iron Age. Earlier, we came across an opin-ion, that its origins need to be searched for among the cultures of Southern Siberia — mainly Khakass-Munusinsk Basin and Eastern Siberia. The discovery of the studied birchbark image in the Novotriotskoye necropolis in-cludes the Upper Ob River region into the potential areas of formation of this tradition.

High Fidelity Makeup via 2D and 3D Identity Preservation Net

In this article, we address the challenging makeup transfer task, aiming to transfer makeup from a reference image to a source image while preserving facial geometry and background consistency. Existing deep neural network-based methods have shown promising results in aligning facial parts and transferring makeup textures. However, they often neglect the facial geometry of the source image, leading to two adverse effects: (1) alterations in geometrically relevant facial features, causing face flattening and loss of personality, and (2) difficulties in maintaining background consistency, as networks cannot clearly determine the face-background boundary. To jointly tackle these issues, we propose the High Fidelity Makeup via two-dimensional (2D) and 3D Identity Preservation Network (IP23-Net), to the best of our knowledge, a novel framework that leverages facial geometry information to generate more realistic results. Our method comprises a 3D Shape Identity Encoder, which extracts identity and 3D shape features. We incorporate a 3D face reconstruction model to ensure the three-dimensional effect of face makeup, thereby preserving the characters’ depth and natural appearance. To preserve background consistency, our Background Correction Decoder automatically predicts an adaptive mask for the source image, distinguishing the foreground and background. In addition to popular benchmarks, we introduce a new large-scale High Resolution Synthetic Makeup Dataset containing 335,230 diverse high-resolution face images to evaluate our method’s generalization ability. Experiments demonstrate that IP23-Net achieves high-fidelity makeup transfer while effectively preserving background consistency. The code will be made publicly available.

Evaluation of the Reproducibility of Auricular Subunit Markers Based on Three-Dimensional Computed Tomography.

As a facial feature, the auricle plays an important role in the integrity and aesthetics of the whole face. Auricular subunits are associated with patient satisfaction in auricular reconstruction, but there are few studies on auricular subunits. We want to evaluate the reproducibility of auricular subunits by measuring the coordinates of the marker points of auricular subunits, accordingly provide a reference for the improvement of auricular reconstruction and the aesthetics of auricular injection. Mimics 19.0 was used to carry out three-dimensional (3D) reconstruction of the computed tomography (CT) scan data of patients' brains; measure the three-dimensional coordinates of the 13 auricular subunit markers, the morphological auricle length and width, and the physiological auricle length and width; and analyze the reproducibility as well as the differences between group. Reproducibility of auricle subunit markers: There are 1124 (58.82%) high reproducibility, 580 (30.35%) moderate reproducibility, and 207 (10.83%) low reproducibility. The superior tragus notch, tragus, and antitragus had the highest reproducibility. There was no significant difference between the groups in the marking points on the helix, and there were no statistically significant differences in the measurement values of the auricles on the two sides. The physiological ear length and width and the morphological ear length of males were larger than those of females. These showed significant differences between the age groups. Most auricular subunit markers have high reproducibility. The subunits with higher reproducibility are the structures that need to be optimized during auricle reconstruction surgery or auricle injection in the future. This journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes Review Articles, Book Reviews, and manuscripts that concern Basic Science, Animal Studies, Cadaver Studies, and Experimental Studies. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Convolutional neural networks to classify human stress that occurs during in‐field sugarcane harvesting: A case study

AbstractAssessing human stress in agriculture proves to be a complex and time‐intensive endeavor within the field of ergonomics, particularly for the development of agricultural systems. This methodology involves the utilization of instrumentation and the establishment of a dedicated laboratory setup. The complexity arises from the need to capture and analyze various physiological and psychological indicators, such as heart rate (HR), muscle activity, and subjective feedback to comprehensively assess the impact of farm operations on subjects. The instrumentation typically includes wearable devices, sensors, and monitoring equipment to gather real‐time data of subject during the performance of farm operations. Deep learning (DL) models currently achieve human performance levels on real‐world face recognition tasks. In this study, we went beyond face recognition and experimented with the recognition of human stress based on facial features during the drudgery‐prone agricultural operation of sugarcane harvesting. This is the first research study for deploying artificial intelligence‐driven DL techniques to identify human stress in agriculture instead of monitoring several ergonomic characteristics. A total of 20 (10 each for male and female) subjects comprising 4300 augmented RGB images (215 per subject) were acquired during sugarcane harvesting seasons and then these images were deployed for training (80%) and validation (20%). Human stress and nonstress states were determined based on four ergonomic physiological parameters: heart rate (ΔHR), oxygen consumption rate (OCR), energy expenditure rate (EER), and acceptable workload (AWL). Stress was defined when ΔHR, OCR, EER, and AWL reached or exceeded certain standard threshold values. Four convolutional neural network‐based DL models (1) DarkNet53, (2) InceptionV3, (3) MobileNetV2 and (4) ResNet50 were selected due to their remarkable feature extraction abilities, simple and effective implementation to edge computation devices. In all four DL models, training performance results delivered training accuracy ranging from 73.8% to 99.1% at combinations of two mini‐batch sizes and four levels of epochs. The maximum training accuracies were 99.1%, 99.0%, 97.7%, and 95.4% at the combination of mini‐batch size 16 and 25 epochs for DarkNet53, InceptionV3, ResNet50, and MobileNetV2, respectively. Due to the best performance, DarkNet53 was tested further on an independent data set of 100 images and found 89.8%–93.3% confident to classify stressed images for female subjects while 92.2%–94.5% for male subjects, though it was trained on the integrated data set. The comparative classification of the developed model and ergonomic measurements for stress classification was carried out with a net accuracy of 88% where there were few instances of wrong classifications.

The Return of the Warrior: Combining Anthropology, Imaging Advances, and Art in Reconstructing the Face of the Early Medieval Skeleton

Reconstructing the face from the skull is important not only for forensic identification but also as a tool that can provide insight into the appearance of individuals from past populations. It requires a multidisciplinary approach that combines anthropological knowledge, advanced imaging methods, and artistic skills. In the present study, we demonstrate this process on the skull of an early medieval warrior from Croatia. The skeletal remains were prepared and scanned using multi-slice computed tomography (MSCT) and examined using standard anthropological and radiological methods. The analysis revealed that the remains belonged to a 35–45-year-old male individual who had suffered severe cranial trauma, probably causing his death. From MSCT images, we reconstructed a three-dimensional (3D) model of the skull, on which we digitally positioned cylinders demarking the soft tissue thickness and created the face with a realistic texture. A 3D model of the face was then optimized, printed, and used to produce a clay model. Sculpturing techniques added skin textures and facial features with scars and trauma manifestations. Finally, after constructing a plaster model, the model was painted and refined by adding fine details like eyes and hair, and it was prepared for presentation in the form of a sculpture.

Human Face Emotion Recognition Using Deep Learning

Human Emotion detection from image is one of the most powerful and challenging research task in social communication. Deep learning based emotion detection gives performance better than traditional methods with image processing. This system is an artificial intelligence systemcapableofemotion detectionthrough facial expressions. It discusses about the procedure of emotion detection, which includes basically three main steps: face detection, features extraction, and emotion classification. This model proposed a convolutionalneural networks(CNN)and uses 3D face models based on deep learning architecture for emotion detection fromimages. Applicationsof face recognition detection extend across diverse fields and addressing challenges related to robustness, privacy, and ethical considerations is essential for the widespread and responsible adoption of face recognition technologyinthesefields.

Identification and classification of Prakriti of human using facial features

<p>Changes in the lifestyle of individual, several of disease has emerged due to imbalance of doshas components. Ayurveda practitioners could identify the imbalance of dosha and relate the root cause of imbalance of doshas. Analysis of dosha varies from practitioner to practitioner and it requires well practiced practitioner to identify dosha. To overcome, the darshana method was adopted to automatically identify predominant dosha using facial features such as face, eyes, nose, and mouth and skin color. Computer vision and Image processing techniques were made attempted in Ayurveda domain, for identification of predominate prakriti, age, and gender of the subject. Eye Aspect Ratio (EAR), Nose Aspect Ratio (NAR), Mouth Aspect Ratio (MAR) and skin color was computed based on Euclidean distance to identify on-live predomaint prakriti of an individual. The values of MAR ≤ 0.5,EAR ≤ 0.1,NAR ≤ 0.8 as identified as vata , 0.5 ≥ MAR ≤ 0.6, 0.1 ≥ EAR ≤ 0.2, 0.8 ≥ NAR ≤ 1 as identified as pitta and MAR ≥ 0.6,EAR ≥ 0.2 and NAR ≥ 1 as identified as kapha dosha. With the features MAR, EAR and NAR classification of predominant prakriti was carried out with an accuracy of 87.5% with support vector classifier.</p>

Privacy-preserving outsourcing scheme of face recognition based on locally linear embedding

Face recognition is a biometric-based technology that identifies or verifies a person’s identity by analyzing and comparing patterns in their facial features. Among kinds of face recognition algorithms, Locally Linear Embedding (LLE) algorithm has the advantage of preserving the local structure of face data. However, the LLE algorithm requires some computationally intensive matrix calculations, which might introduce latency to the device. In this paper, we propose a privacy-preserving outsourcing scheme of face recognition based on LLE algorithm for the first time. By outsourcing matrix multiplication, linear equations, and eigenvalue decomposition of the LLE algorithm to a cloud server, we reduce the computational load on the client device. To ensure data security, we generate a novel type of sparse orthogonal matrix through square root application and random permutation of diagonal elements, used as secret keys to blind the client’s data. Meanwhile, due to the sparsity of key matrices, the complexity of client-side computation complexity is reduced from O(n3) to O(n2). The experimental results show that our proposed scheme reduces computational costs while maintaining almost identical face recognition accuracy as the original LLE-based algorithm.

Development and validation of point-of-care mobile solution to guide the diagnosis of malnutrition: A multicenter, prospective cohort study.

11042 Background: Malnutrition has negative effects on patients with chronic diseases, leading to reduced treatment tolerance, increased risk of clinical complications, and even death. This research was aimed to develop a point-of-care program based on facial features to screen malnourished inpatient patients. Methods: In this prospective, multicenter, cohort study, we retrieved facial photograph and malnutrition screening scales from 4 different hospitals. We utilized a variety of machine learning models to explore whether the ocular area could serve as an enhanced region for facial recognition nutrition. Last, we utilized a facial area segmentation and weighted approach to retain the information of full-face features using a BP neural network and validated using Delong-test, IDI-test, and NRI-test. Overall, 619 inpatients’ facial photographs and their corresponding nutrition screening questionnaires were used to train, validate and test the machine learning model. Results: The Pearson correlation analysis showed a significant correlation (p<0.05) between the two questionnaires in all groups. The average AUC obtained from the five-fold cross-validation set was 0.886 (CI 0.843-0.930), 0.834 (CI 0.764-0.904), and 0.927 (CI 0.899-0.955) for the Cancer Inpatient Group, Other Inpatient Group, and All Inpatient Group, respectively, with the corresponding AUC obtained from the external validation set being 0.860 (CI 0.817-0.904), 0.843 (CI 0.796-0.889), and 0.887 (CI 0.829-0.944). Conclusions: The facial photograph-based point of-care mobile solution can screen malnutrition with good accuracy, showing its potential for screening malnutrition in inpatients in the hospital in different types of diseases.

Low-resolution facial emotion recognition on low-cost devices

The low-resolution input image is a crucial challenge for applying facial emotion recognition in real-world scenarios. The critical problem is that valuable object features are relatively lost in the extraction process due to their small size. On the other hand, this vision system is required by a machine to run smoothly on low-cost devices. Facial emotion recognition using a lightweight feature extractor is proposed in this study to effectively capture crucial facial components in a low-resolution image. To compromise the running speed, this work offers an efficient feature convolution to discriminate specific facial features. In addition, the system is embedded with an attentive module to capture important features and correlate them. Our model performance is evaluated on low-resolution public datasets achieving the accuracy of 97.34\%, 81.10\%, and 80.12\% on KDEF, RFDB, and FER-plus, respectively. The practical application demands that the deep learning model can operate fast on inexpensive devices. Consequently, the model achieved a speed of 290 FPS on a CPU device.

Enhancing Driver Safety: Deep Learning Approach for Drowsiness Detection and Accident Prevention

Road safety concerns have spurred the development of innovative technologies aimed at reducing accidents, particularly those caused by driver fatigue. The main scope of the driver drowsiness detection system is to minimize road accidents caused by fatigue or sleepiness of drivers. This system leverages deep learning and computer vision, employing a Raspberry Pi camera to monitor facial expressions, including yawning, to assess the driver's alertness. Upon detecting signs of drowsiness, such as prolonged eye closure or altered facial expressions, the system triggers a buzzer alert by analyzing the Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR) ratios. Integrated within an embedded system, it utilizes frontal face detection algorithms and Haar cascade classifiers to localize key facial features in real-time, facilitating efficient monitoring for signs of fatigue or distraction. Additionally, it issues vibration alerts if the seat belt is not fastened. The seat belt remainder will be helpful to ensure the safety of the driver, reduces accidents and monitors the driver's heart rate, triggering a PANIC message if irregularities are detected. At traffic signals, the system automatically reduces vehicle speed using a vibration motor. In the unfortunate event of an accident, the system initiates speed reduction and promptly notifies registered contacts through the Blynk application, thereby significantly reducing accidents caused by drowsy driving.

Eye Strain Expression Classification using Attention Capsule Network for Adapting Screen Vision

Beside the conventional facial expression recognition methods, the research focuses on developing a system for recognizing various eye expressions under different screen conditions. This research deals with the use of Capsule Network (a recent Deep Learning algorithm) to enhance facial expression recognition capabilities and to develop adaptive screen technologies aimed at mitigating digital eye strain. The main objective of this research is to engineer a sophisticated system that employs the capabilities of Capsule Nets to recognize the various expressions that user makes and based on the recognized expression, dynamically modify screen settings, ensuring optimal user visual comfort. The research primarily concentrates on the exploration and application of various Capsule Net architectures designed for the recognition of expressions related to eye strain. The baseline model utilized elementary convolutional layers which feed into subsequent fully connected layers for the task of classification. The model has since been refined by incorporating advanced techniques such as attention mechanisms and more sophisticated network architectures where the classification is done by Capsule Network. Results have demonstrated a modest enhancement in the Capsule Net’s predictive performance, attributed to its superior spatial and hierarchical processing of facial features, in comparison to conventional deep learning approaches. The final model has an accuracy of 82.27%. As a final system the model has been deployed to an application to process frames from video camera in the device and make prediction to prompt the notifications or recommendations.

Loss-of-function variants in ERF are associated with a Noonan syndrome-like phenotype with or without craniosynostosis.

Pathogenic, largely truncating variants in the ETS2 repressor factor (ERF) gene, encoding a transcriptional regulator negatively controlling RAS-MAPK signaling, have been associated with syndromic craniosynostosis involving various cranial sutures and Chitayat syndrome, an ultrarare condition with respiratory distress, skeletal anomalies, and facial dysmorphism. Recently, a single patient with craniosynostosis and a phenotype resembling Noonan syndrome (NS), the most common disorder among the RASopathies, was reported to carry a de novo loss-of-function variant in ERF. Here, we clinically profile 26 individuals from 15 unrelated families carrying different germline heterozygous variants in ERF and showing a phenotype reminiscent of NS. The majority of subjects presented with a variable degree of global developmental and/or language delay. Their shared facial features included absolute/relative macrocephaly, high forehead, hypertelorism, palpebral ptosis, wide nasal bridge, and low-set/posteriorly angulated ears. Stature was below the 3rd centile in two-third of the individuals, while no subject showed typical NS cardiac involvement. Notably, craniosynostosis was documented only in three unrelated individuals, while a dolichocephalic aspect of the skull in absence of any other evidence supporting a premature closing of sutures was observed in other 10 subjects. Unilateral Wilms tumor was diagnosed in one individual. Most cases were familial, indicating an overall low impact on fitness. Variants were nonsense and frameshift changes, supporting ERF haploinsufficiency. These findings provide evidence that heterozygous loss-of-function variants in ERF cause a "RASopathy" resembling NS with or without craniosynostosis, and allow a first dissection of the molecular circuits contributing to MAPK signaling pleiotropy.

Clinical and molecular characteristics of Korean patients with Kabuki syndrome.

Kabuki syndrome (KS) is a rare disorder characterized by typical facial features, skeletal anomalies, fetal fingertip pad persistence, postnatal growth retardation, and intellectual disabilities. Heterozygous variants of the KMT2D and KDM6A genes are major genetic causes of KS. This study aimed to report the clinical and genetic characteristics of KS. This study included 28 Korean patients (14 boys and 14 girls) with KS through molecular genetic testing, including direct Sanger sequencing, whole-exome sequencing, or whole-genome sequencing. The median age at clinical diagnosis was 18.5 months (IQR 7-58 months), and the median follow-up duration was 80.5 months (IQR 48-112 months). Molecular genetic testing identified different pathogenic variants of the KMT2D (n = 23) and KDM6A (n = 3) genes, including 15 novel variants. Patients showed typical facial features (100%), such as long palpebral fissure and eversion of the lower eyelid; intellectual disability/developmental delay (96%); short stature (79%); and congenital cardiac anomalies (75%). Although 71% experienced failure to thrive in infancy, 54% of patients showed a tendency toward overweight/obesity in early childhood. Patients with KDM6A variants demonstrated severe genotype-phenotype correlation. This study enhances the understanding of the clinical and genetic characteristics of KS.

Face Detection under Hostile Circumstances using Innovative Robust Methods

Abstract: An attendance system is crucial for monitoring student presence in classes, with various methods available such as biometric, RFID card, face recognition, and traditional paper-based systems. Among these, face recognition stands out for its security and efficiency. This research focuses on enhancing the face recognition attendance system's accuracy by minimizing false positives through a confidence threshold based on the Euclidean distance metric. The Local Binary Pattern Histogram (LBPH) algorithm outperforms other distance-based methods like Eigenfaces and Fisherfaces due to its robustness against grayscale transformations. Face detection relies on Haar cascades for their robustness, while LBPH is employed for recognition. Leveraging LBPH for recognition tasks significantly improves the system’s ability to discern subtle variations in facial features, thereby enhancing overall accuracy. Moreover, the effectiveness of the system heavily relies on robust face detection techniques. Haar cascades are employed for this purpose, leveraging their proven reliability in detecting faces across diverse environmental conditions and camera angles. The system achieves a recognition rate of 77% for students and a false- positive rate of 28%. Notably, it effectively recognizes students even with variations like wearing glasses or facial hair. Face recognition for unknown individuals is approximately 60%,with false-positive rates of 14% and 30% with and without the threshold, respectively.

Driver Drowsiness Detection using CNN

Abstract: Driver drowsiness is a significant factor contributing to road accidents worldwide, posing a major threat to public safety. This project presents a robust and efficient approach to detecting driver drowsiness using Convolutional Neural Networks (CNN). The proposed system aims to enhance road safety by monitoring drivers in real-time and providing timely warnings to prevent accidents caused by fatigue. The methodology involves capturing video frames of the driver's face using a camera installed in the vehicle. The CNN model is trained on a comprehensive dataset containing images of alert and drowsy states. Key facial landmarks and features, such as eye closure duration, yawning frequency, and head position, are extracted and analyzed to determine the driver's level of alertness. By leveraging the powerful feature extraction capabilities of CNNs, the system can accurately distinguish between alert and drowsy states with high precision. Experimental results demonstrate the effectiveness of the proposed system in real-world scenarios, achieving a high accuracy rate in detecting drowsiness. The system's performance is evaluated against various benchmarks, showcasing its potential for integration into modern vehicles as a preventive safety measure. The deployment of this CNN-based drowsiness detection system can significantly reduce the risk of accidents, contributing to safer driving conditions and saving lives.

Drowsy Driver Detection System

Abstract: Drowsy driver detection system is a safety technology designed to identify the potential signs of fatigue or drowsiness of the driver to prevent accidents caused as a result of not being alert. It is a significant risk factor for traffic accidents which has led to thousands of accidents, injuries, and fatalities each year. In this system we have used some techniques to monitor the driver’s behavior or state. This involves tracking facial features, eye movements or blink patterns and yawning. The system identifies signs of fatigue, such as slow eyelid closure, increased blink duration and yawn which often indicate drowsiness. As technology advances, these systems are becoming more accurate, efficient and integrated with other vehicle safety features. The continued development of drowsy driver detection technology holds significant promise for reducing traffic accidents and improving overall road safety.

Review of Challenges and Innovations in Occluded Facial Recognition in Disguise and Crowd

Abstract: Facial recognition technology is critical in modern security and identification systems, yet it faces significant challenges when dealing with occluded faces, whether due to disguises or in crowds. This review explores the complexities of occluded facial recognition, highlighting the obstacles presented by partial or complete obstruction of facial features by various elements like clothing, accessories, or other people in crowded spaces. These occlusions, intentional or otherwise, complicate the identification process, rendering traditional recognition systems less effective. Recent advancements in machine learning, particularly through Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), have ushered in innovative solutions that enhance the system's ability to recognize faces even when significant portions are obscured. These developments signify a move towards more adaptable and precise occluded facial recognition technologies, essential for real-world applications where occlusions are unpredictable. The paper discusses ongoing challenges and innovations considerations, various occluded system performance metrics and conclude with way ahead for Researchers and Practioners

Diagnosis of 7q11.23 deletion in a patient from Manaus, Amazonas with Williams-Beuren syndrome: Case report

Williams-Beuren Syndrome is a rare disease caused by a chromosomal microdeletion in region 7q11.23. It is characterized as multisystemic, including intellectual and learning disabilities, distinct facial features, short stature, hypersocial behavior, and many malformations. Its diagnosis is traditionally clinical, but cytogenomic analyses such as fluorescence in situ hybridization can also be used for better etiological characterization of the syndrome. Our objective is to report the case of a boy in Manaus/Amazonas with Williams-Beuren Syndrome that presents a microdeletion in region 7q11.23. The patient is male, 12 years, of non-consanguineal parents, and no family history of hereditary diseases. The patient was sent for genetic testing after assessment of possible genetic syndrome, as the physical exams presented a gnome-like face, short nasal bridge, full lips, frequent smile, short stature, difficulty learning and atrioventricular dysplasia of valves with mild insufficiency and a cist in the left kidney. The results of the karyotype exam were normal and the fluorescence in situ hybridization revealed the microdeletion of region 7q11.23. Upon the follow-up, the subject was diagnosed with Williams-Beuren Syndrome. This microdeletion results in the loss of various genes that affect genic dosage/genic haploinsufficiency, resulting in a cytogenetic imbalance, consequently contributing to the patient’s phenotype. Therefore, the cytogenomic analysis was instrumental for the etiological diagnosis, in which microdeletion 7q11.23 was identified, allowing for proper diagnosis and prognosis, genetic counseling, and medical decisions, granting a better quality of life for the patient.