Facial Features Research Articles

Expanding the phenotype of neurofibromatosis type 1 microdeletion syndrome.

Neurofibromatosis type 1 (NF-1) microdeletion syndrome accounts for 5 to 11% of individuals with NF-1. The aim of our study was to characterize a large cohort of individuals with NF-1 microdeletion syndrome and expand its natural history. We conducted a retrospective chart review from 1994 to 2024 of individuals with NF-1 microdeletion syndrome followed at two large Neurofibromatosis Clinics. This cohort consists of 57 individuals with NF-1 microdeletion syndrome (28 type-1, 4 type-2, 2 type-3, 9 atypical deletions, and 14 indeterminate). We note 38/56 (67.9%) with describable facial features, 25/57 (43.8%) with plexiform neurofibromas, and 3/57 (5.2%) with malignant peripheral nerve sheath tumors within the observed period. The most reported neurodevelopmental manifestations from school-age or older individuals included 39/49 (79.6%) with developmental delays, 35/49 (71.4%) with expressive and/or receptive speech delays, 33/41 (80.5%) with learning difficulties, and 23/42 (54.8%) with attention-deficit/hyperactivity disorder. Full-scale IQ testing data was available for 22 individuals (range: 50-96). Of the 21 adults in this cohort, 14/21 (66.7%) graduated from high school, and 4/21 (19.0%) had some college experience. Many individuals received academic support (i.e., special education, individual education plan). In this cohort, neurocognitive outcomes in adults varied more than typically reported in the literature.

Amphibious ethics and speculative immersions: laboratory aquariums as a site for developing a more inclusive animal geography

ABSTRACT Human capacity to sense and respond to the suffering of non-human animals is key to animal care and welfare. Intuitively these modes of relating seem best suited to interactions between humans and warm-blooded mammals who share human-like facial features and characteristics. Animal geographers and those working in animal welfare have noted the challenges that humans face in learning to care about fishes, and how this leads to welfare guidelines and regulations which are poorly suited to aquatic species. This paper draws on interviews with laboratory aquarists and biomedical researchers to explore how they have learnt to sense and respond to the needs of fishes in the laboratory. We offer two key observations. Firstly, despite significant bodily differences, humans find ways to empathise with fishes. Secondly, whilst observations of bodies and behaviours predominate in laboratory mammal welfare assessments, when working with fishes water quality serves as an important proxy for species health. We conclude that the laboratory aquarium signifies methodological and conceptual limits in contemporary animal geographies. We further argue that these barriers should be understood as cultural, and – as we demonstrate – that there is consequently scope and capacity to reach beyond them by engaging in amphibious ethics and speculative immersions.

Constructing a cataplexy face prediction model for narcolepsy type 1 based on ResNet-18

Objective: To establish a prediction model for the identifying of cataplexy facial features based on clinical shooting videos by using a deep learning image recognition network ResNet-18. Methods: A cross-sectional study. Twenty-five narcolepsy type 1 patients who were first diagnosed and never received treatment and 25 healthy controls recruited by advertisement in the Second Affiliated Hospital of Nanchang University from 2020 to 2023.After image preprocessing, a total of 1 180 images were obtained, including 583 cataplexy faces and 597 normal faces.90% were selected as the training set and validation set, and then expanded the data by 5 times.80% of the expanded data set was extracted as the training set and 20% as the validation set, that is, the number of the training set was (583+597)×0.9×0.8×5=4 248, the number of the validation set was (583+597)×0.9×0.2×5=1 062. The data sets for training and validation were used train parameters to establish the model and were trained through the five-fold cross-validation method, to establish the ResNet-18 cataplexy face recognition model via transfer learning.10% (118 images) of the original non-amplified images were extracted as the test set. The test set data did not participate in data enhancement and model training, and was only used to evaluate the final performance of the model. Finally, ResNet-18 was compared with VGG-16, ResNet-34 and Inception V3 deep learning models, and the receiver operating characteristic curve was used to evaluate the value of ResNet-18 image recognition network in cataplexy face recognition. Results: Among 25 patients with narcolepsy type 1, 15 were males and 10 were females, aged [M (Q1, Q3)] of 14.0(11.0, 20.5) years.Among 25 healthy controls, 14 were males and 11 were females, with a median age of 16.0(14.4, 23.0) years.The overall accuracy of ResNet-18 image recognition network in the test set was 90.9%, the sensitivity was 96.4% and the specificity was 85.2%. The area under the ROC curve was 0.99(95%CI:0.96-1.00). The ResNet-18 model parameter amount was 11.69 M, the floating point operation amount was 1 824.03 M, and the single image recognition time was 5.9 ms. Conclusions: The cataplexy face prediction model built based on the deep learning image recognition network ResNet-18 has a high accuracy in identifying cataplexy faces.

Three Familial Cases of Stickler Syndrome: A Case Report

Purpose: Stickler syndrome, a hereditary connective tissue disorder characterized by mutations in collagen genes, presents with progressive ophthalmopathy and diverse systemic manifestations. Here, we present three familial cases of Stickler syndrome, emphasizing the importance of early detection through clinical investigations and genetic testing.Case summary: Two generations of a family, a mother and her two daughters, were evaluated for Stickler syndrome. All three exhibited bilateral retinal lattice degeneration, perivascular retinal degeneration, and vitreous liquefaction. The daughters shared characteristic facial features, including a flattened face, broad nasal bridge, and micrognathia. Clinical symptoms and examination findings led to diagnoses of Stickler syndrome. Subsequent genetic testing in five family members confirmed a COL2A1 mutation in the three affected individuals.Conclusions: Stickler syndrome carries a high risk of vision loss from ocular complications, necessitating early detection and intervention. In addition, the presence of systemic manifestations, such as musculoskeletal joint disorders, mitral valve prolapse, hearing loss, and cleft palate, emphasizes the importance of prompt detection through appropriate clinical investigations and genetic testing.

Case Report: Two different acromelic dysplasia phenotypes in a Chinese family caused by a missense mutation in FBN1 and a literature review

BackgroundAcromelic dysplasia caused by FBN1 mutation includes acromicric dysplasia (AD), geleophysic dysplasia 2 (GD2), and Weill-Marchesani syndrome 2 (WMS2). All three diseases share severe short stature and brachydactyly. Besides phenotypic similarity, there is a molecular genetic overlap among them, as identical FBN1 gene mutations have been identified in patients with AD, GD2, and WMS2. However, no family with different acromelic dysplasia phenotypes due to the same variant has been described in English reports.Case reportThe proband presented with typical facial features, severe short stature, short limbs, stubby hands and feet and radiological abnormalities. Her elder sister and mother had similar physical features. In addition, her elder sister was found to have aortic valve stenosis by echocardiography. Mutation analysis demonstrated a heterozygous missense mutation, c.5179C>T (p.Arg1727Trp) in exon 42 of the FBN1. The proband and her mother were diagnosed with AD, and her elder sister with GD2. The proband was treated with recombinant human growth hormone (rhGH) and had a body length gain of 0.72 SDS in half a year.ConclusionThese findings expand the phenotypic spectrum of FBN1 gene mutations and highlight that identical FBN1 genotypes can result in different phenotypes of acromelic dysplasia in a family. The efficacy of rhGH therapy in patients with acromelic dysplasia is controversial. More follow-up is needed on the long-term efficacy of rhGH therapy.

Video and Audio Deepfake Datasets and Open Issues in Deepfake Technology: Being Ahead of the Curve

The revolutionary breakthroughs in Machine Learning (ML) and Artificial Intelligence (AI) are extensively being harnessed across a diverse range of domains, e.g., forensic science, healthcare, virtual assistants, cybersecurity, and robotics. On the flip side, they can also be exploited for negative purposes, like producing authentic-looking fake news that propagates misinformation and diminishes public trust. Deepfakes pertain to audio or visual multimedia contents that have been artificially synthesized or digitally modified through the application of deep neural networks. Deepfakes can be employed for benign purposes (e.g., refinement of face pictures for optimal magazine cover quality) or malicious intentions (e.g., superimposing faces onto explicit image/video to harm individuals producing fake audio recordings of public figures making inflammatory statements to damage their reputation). With mobile devices and user-friendly audio and visual editing tools at hand, even non-experts can effortlessly craft intricate deepfakes and digitally altered audio and facial features. This presents challenges to contemporary computer forensic tools and human examiners, including common individuals and digital forensic investigators. There is a perpetual battle between attackers armed with deepfake generators and defenders utilizing deepfake detectors. This paper first comprehensively reviews existing image, video, and audio deepfake databases with the aim of propelling next-generation deepfake detectors for enhanced accuracy, generalization, robustness, and explainability. Then, the paper delves deeply into open challenges and potential avenues for research in the audio and video deepfake generation and mitigation field. The aspiration for this article is to complement prior studies and assist newcomers, researchers, engineers, and practitioners in gaining a deeper understanding and in the development of innovative deepfake technologies.

Facial Frontiers: Unveiling the Potential of LBPHs and Haar Cascades in Facial Recognition for Enhanced School Security

The article aims at introducing a Facial Recognition School Security System as one solution to improve security while speeding up administrative processes in educational institutions. The need for such a system emanates from concerns over the safety of schools as well as inadequacies inherent in conventional attendance and access control methods. Inadequate methods can be manual or rely on an older technology that leads to inefficiencies, inaccuracies and breaches of security. This proposed solution exploits contemporary artificial intelligence algorithms and computer vision techniques to facilitate the reliable identification and validation of entrants thereby providing contactless approach during Pandemic times in compliance with the COVID-19 safety protocols. At this period of COVID-19 it also acts as an avenue where physical touchpoints are reduced with consideration to social distancing measures. Our method is novel in the fact that it only detects and recognizes human faces as opposed to general object detection systems. We use Local Binary Pattern Histograms (LBPH) for face recognition and Haar Cascades for face detection. The Haar Cascade algorithm employs simple rectangular features to detect faces, using a cascade of weak classifiers to achieve high detection rates. The LBPH algorithm captures local texture patterns of facial features, calculating LBP values for each pixel. Our project demonstrates variable performance across different classes, with precision ranging from 0.50 to 1.00, recall from 0.33 to 1.00, and F1 scores from 0.33 to 0.94, while achieving an overall accuracy of 0.75, indicating robust performance in certain scenarios but room for improvement in others.

A multi-ancestry GWAS meta-analysis of facial features and its application in predicting archaic human features

Facial morphology, a complex trait influenced by genetics, holds great significance in evolutionary research. However, due to limited fossil evidence, the facial characteristics of Neanderthals and Denisovans have remained largely unknown. In this study, we conducted a large-scale multi-ethnic meta-analysis of Genome-Wide Association Study (GWAS), including 9674 East Asians and 10,115 Europeans, quantitatively assessing 78 facial traits using 3D facial images. We identified 71 genomic loci associated with facial features, including 21 novel loci. We developed a facial polygenic score (FPS) that enables the prediction of facial features based on genetic information. Interestingly, the distribution of FPSs among populations from diverse continental groups exhibited significant correlations with observed facial features. Furthermore, we applied the FPS to predict the facial traits of seven Neanderthals and one Denisovan using ancient DNA, and aligned predictions with the fossil records. Our results suggested that Neanderthals and Denisovans likely shared similar facial features, such as a wider but shorter nose and a wider endocanthion distance. The decreased mouth width was characterized specifically in Denisovan. The integration of genomic data and facial trait analysis provides valuable insights into the evolutionary history and adaptive changes in human facial morphology.

Open-Set Sheep Face Recognition in Multi-View Based on Li-SheepFaceNet

Deep learning-based sheep face recognition improves the efficiency and effectiveness of individual sheep recognition and provides technical support for the development of intelligent livestock farming. However, frequent changes within the flock and variations in facial features in different views significantly affect the practical application of sheep face recognition. In this study, we proposed the Li-SheepFaceNet, a method for open-set sheep face recognition in multi-view. Specifically, we employed the Seesaw block to construct a lightweight model called SheepFaceNet, which significantly improves both performance and efficiency. To enhance the convergence and performance of low-dimensional embedded feature learning, we used Li-ArcFace as the loss function. The Li-SheepFaceNet achieves an open-set recognition accuracy of 96.13% on a self-built dataset containing 3801 multi-view face images of 212 Ujumqin sheep, which surpasses other open-set sheep face recognition methods. To evaluate the robustness and generalization of our approach, we conducted performance testing on a publicly available dataset, achieving a recognition accuracy of 93.33%. Deploying Li-SheepFaceNet on an open-set sheep face recognition system enables the rapid and accurate identification of individual sheep, thereby accelerating the development of intelligent sheep farming.

Trichohepatoenteric syndrome type 1: expanding the clinical spectrum of THES type 1 due to a homozygous variant in the SKIC3 gene

Trichohepatoenteric syndrome (THES), also known as phenotypic diarrhea or syndromic diarrhea, is a rare autosomal recessive genetic disorder caused by mutations in SKIC2 (THES-type 2) or SKIC3 (THES-type 1) and is characterized by early onset diarrhea, woolly brittle hair, facial dysmorphic features and liver disease. We report the case of a 24-month-old girl who presented with chronic diarrhea since the neonatal period along with intrauterine growth restriction (IUGR), developmental delay, dysmorphic features, congenital heart defects, liver disease, and recurrent infections. The diagnosis was made through whole-exome sequencing analysis, which detected a homozygous variant (c.4070del, p.Pro1357Leufs*10) in the SKIC3 gene. The patient required parenteral nutrition and was hospitalized for the first 10 months of life and then discharged on PN after showing improvement. She remained stable on PN after discharge despite a few admissions for central line infections. Recent follow-up at the age of 2 years revealed that she was stable on long-term parenteral nutrition and that she had advanced chronic liver disease.

A Novel Hybrid Attention-Based Dilated Network for Depression Classification Model from Multimodal Data Using Improved Heuristic Approach

Automatic depression classification from multimodal input data is a challenging task. Modern methods use paralinguistic information such as audio and video signals. Using linguistic information such as speech signals and text data for depression classification is a complicated task in deep learning models. Best audio and video features are built to produce a dependable depression classification system. Textual signals related to depression classification are analyzed using text-based content data. Moreover, to increase the achievements of the depression classification system, audio, visual, and text descriptors are used. So, a deep learning-based depression classification model is developed to detect the person with depression from multimodal data. The EEG signal, Speech signal, video, and text are gathered from standard databases. Four stages of feature extraction take place. In the first stage, the features from the decomposed EEG signals are attained by the empirical mode decomposition (EMD) method, and features are extracted by means of linear and nonlinear feature extraction. In the second stage, the spectral features of the speech signals from the Mel-frequency cepstral coefficients (MFCC) are extracted. In the third stage, the facial texture features from the input video are extracted. In the fourth stage of feature extraction, the input text data are pre-processed, and from the pre-processed data, the textual features are extracted by using the Transformer Net. All four sets of features are optimally selected and combined with the optimal weights to get the weighted fused features using the enhanced mountaineering team-based optimization algorithm (EMTOA). The optimal weighted fused features are finally given to the hybrid attention-based dilated network (HADN). The HDAN is developed by combining temporal convolutional network (TCN) with bidirectional long short-term memory (Bi-LSTM). The parameters in the HDAN are optimized with the assistance of the developed EMTOA algorithm. At last, the classified output of depression is obtained from the HDAN. The efficiency of the developed deep learning HDAN is validated by comparing it with various traditional classification models.

An Approach to Selection of Face-Lift Techniques for Different Types of Faces: An Analysis of 1000 Asian Patients Over 9 Years.

A face-lift or rhytidectomy is the procedure most directly associated with rejuvenation. There are several surgical techniques for face-lifts, but criteria for the selection of techniques, based on the patient's face shape, are lacking. In this study, we report on our experience with specific indication criteria for each technique and the consequent achievement of good outcomes. From 2015 to 2023, 1 surgeon performed face-lifts on 1000 patients. Three different superficial musculoaponeurotic system (SMAS) techniques (SMAS dissection, SMASectomy, and SMAS plication) were applied depending on the degree of sagging of the patient's lower face, lateral facial profile, and SMAS mobility and condition. Superficial musculoaponeurotic system dissection was considered for the improvement of a square face, sagging jowls, and marionette lines. Superficial musculoaponeurotic system plication was applied with patients with less sagging jowls and prominent zygoma with concave lateral facial profile. SMASectomy was applied with patients with convex lateral facial profiles or when the SMAS was too thin or damaged. Patient postoperative satisfaction was surveyed using the FACE-Q score 1 year post surgery. Most of the patients attained natural-looking and long-lasting aesthetic outcomes and exhibited high satisfaction. The patients indicated that they looked about 11.2 ± 5.2 years younger than their actual age after the surgery. The mean satisfaction scores for each facial feature were as follows: cheeks (91.1 ± 7.8), marionette lines (88.5 ± 13.6), lower face and jawline (92.5 ± 14.2), under chin (87.8 ± 15.1), and neck (86.2 ± 18.5). Complications such as facial nerve injury, infection, hematoma, and flap necrosis were very rare. Establishing criteria for the selection of face-lift surgical techniques based on the degree of lower face sagging, lateral facial profile, and SMAS mobility and condition led to good outcomes. These criteria can be used by physicians to determine the most effective face-lift surgery technique based on a patient's individual features, which may improve surgical outcomes.

An Approach to Selection of Face-Lift Techniques for Different Types of Faces: An Analysis of 1000 Asian Patients Over 9 Years.

A face-lift or rhytidectomy is the procedure most directly associated with rejuvenation. There are several surgical techniques for face-lifts, but criteria for the selection of techniques, based on the patient's face shape, are lacking. In this study, we report on our experience with specific indication criteria for each technique and the consequent achievement of good outcomes. From 2015 to 2023, 1 surgeon performed face-lifts on 1000 patients. Three different superficial musculoaponeurotic system (SMAS) techniques (SMAS dissection, SMASectomy, and SMAS plication) were applied depending on the degree of sagging of the patient's lower face, lateral facial profile, and SMAS mobility and condition. Superficial musculoaponeurotic system dissection was considered for the improvement of a square face, sagging jowls, and marionette lines. Superficial musculoaponeurotic system plication was applied with patients with less sagging jowls and prominent zygoma with concave lateral facial profile. SMASectomy was applied with patients with convex lateral facial profiles or when the SMAS was too thin or damaged. Patient postoperative satisfaction was surveyed using the FACE-Q score 1 year post surgery. Most of the patients attained natural-looking and long-lasting aesthetic outcomes and exhibited high satisfaction. The patients indicated that they looked about 11.2 ± 5.2 years younger than their actual age after the surgery. The mean satisfaction scores for each facial feature were as follows: cheeks (91.1 ± 7.8), marionette lines (88.5 ± 13.6), lower face and jawline (92.5 ± 14.2), under chin (87.8 ± 15.1), and neck (86.2 ± 18.5). Complications such as facial nerve injury, infection, hematoma, and flap necrosis were very rare. Establishing criteria for the selection of face-lift surgical techniques based on the degree of lower face sagging, lateral facial profile, and SMAS mobility and condition led to good outcomes. These criteria can be used by physicians to determine the most effective face-lift surgery technique based on a patient's individual features, which may improve surgical outcomes.

Advances in personalized modelling and virtual display of ethnic clothing for intelligent customization

Abstract To enhance the intelligence of the ethnic clothing customization industry and provide online solutions for clothing ordering and fashion shows, this study analyses the latest developments in the fields of digital human models, 3D clothing models, and virtual clothing displays in recent years. This study focuses on analysing the categories and processes of three related technologies in clothing virtual display and compares the advantages and disadvantages of four human body modelling methods. Reshaping the facial features of a 3D body scan model and binding the bones of each part is key to providing a personalized body model. In addition, 3D clothing modelling technology is classified into three methods. The 3D fabric scan data are used to establish realistic seam information and process details simulation and use hybrid modelling methods to virtually sew 2D samples, which can enhance the realism of clothing models. The virtual display platform for ethnic clothing is constructed through layered design based on the Unity3D engine, matching versions and virtual display. The above research ideas can solve bottleneck problems in clothing customization. This has important guiding significance for opening up the ethnic clothing customization market, promoting the intelligence of the customization industry, and promoting ethnic clothing culture.

Autism detection in children based on facial image data using RPY axial facial features and Dual Phase Net model

Autism detection in children based on facial image data using RPY axial facial features and Dual Phase Net model

Achieving natural facial harmonisation: a clinical review of Restylane facial filler

Facial fillers have become integral to aesthetic medicine, significantly enhancing facial features and restoring a youthful appearance ( Evans, 2019 ). Among the available fillers, achieving a ‘natural look’ is paramount to maintaining facial harmonisation ( Davis, 2020 ). This natural look ensures subtle enhancements that do not compromise an individual's inherent expressions and features ( Wilson, 2022 ). The increasing demand for natural-looking results underscores the necessity for products that deliver reliable, safe, and aesthetically pleasing outcomes ( Thompson and Robinson, 2021 ).

Quantification of Cephalometric Changes in Gonial Angle Morphology Following Facial Feminization Surgery.

Facial feminization surgery (FFS) is a type of gender-affirming surgery aimed at bringing masculine facial features more in line with typically feminine characteristics. Specifically, mandibular contouring can create a softer jawline and help create a more round, feminine face. As the popularity of FFS continues to increase, improving surgical techniques and patient satisfaction is imperative. However, no quantitative measurement system currently exists to measure these changes. In this study, the authors describe the use of a novel segmentation technique using computerized tomography imaging to quantify the bony changes that occur during gonial angle reduction. Further, authors utilize this technique to describe changes in a cohort of 13 patients, and how these changes correlate with patient satisfaction. The authors found that gonial angle volume and surface area significantly decreased, as well as the intergonial:interzygomatic ratio, with a smaller ratio associated with more feminine features. In addition, patient satisfaction significantly increased post-operatively both specifically regarding jawline appearance (P = 0.0014) and regarding overall social and psychological function (P = 0.0021 and P = 0.0032, respectively), as captured by the FACE-Q and World Health Organization Quality of Life (WHOQOL) surveys. Patients with greater changes in surface area reported greater improvements in WHOQOL psychological scores (P = 0.0086), and patients with greater changes in the intergonial:interzygomatic ratio reported greater improvements in WHOQOL social scores (P = 0.0299). Overall, our novel technique captures significant changes in gonial angle shape and can be applied to a wide range of future studies to improve the quality and accessibility of FFS.

Chronic paternal alcohol exposures induce dose-dependent changes in offspring craniofacial shape and symmetry.

Although dose-response analyses are a fundamental tool in developmental toxicology, few studies have examined the impacts of toxicant dose on the non-genetic paternal inheritance of offspring disease and dysgenesis. In this study, we used geometric morphometric analyses to examine the impacts of different levels of preconception paternal alcohol exposure on offspring craniofacial shape and symmetry in a mouse model. Procrustes ANOVA followed by canonical variant analysis of geometric facial relationships revealed that Low-, Medium-, and High-dose treatments each induced distinct changes in craniofacial shape and symmetry. Our analyses identified a dose threshold between 1.543 and 2.321g/kg/day. Below this threshold, preconception paternal alcohol exposure induced changes in facial shape, including a right shift in facial features. In contrast, above this threshold, paternal exposures caused shifts in both shape and center, disrupting facial symmetry. Consistent with previous clinical studies, changes in craniofacial shape predominantly mapped to regions in the lower portion of the face, including the mandible (lower jaw) and maxilla (upper jaw). Notably, high-dose exposures also impacted the positioning of the right eye. Our studies reveal that paternal alcohol use may be an unrecognized factor contributing to the incidence and severity of alcohol-related craniofacial defects, complicating diagnostics of fetal alcohol spectrum disorders.

Thermal comfort prediction in multi-occupant spaces based on facial temperature and human attributes identification

Research on thermal comfort prediction has garnered significant attention, with numerous studies focusing on predicting individual thermal comfort through various physiological indicators. However, these methods often lack reliability in long-distance, multi-occupant spaces and fail to utilize RGB image information fully. To address these limitations, this study introduces a new method for predicting thermal comfort in multi-occupant spaces by integrating individual attributes and facial temperature features. The research extracts human body attributes from RGB images and temperature features from different facial regions using image registration and deep learning algorithms. Results show that specific facial regions (cheeks, nose, eyes) combined with human attributes effectively predict thermal comfort. In contrast, the forehead, mouth, and chin are less reliable due to their sensitivity to external influences. Among various models, the LightGBM model achieves the highest accuracy at 90.04 ± 4.46%, demonstrating the effectiveness of the model this study proposed. This study offers a novel perspective for thermal comfort prediction in various settings, such as classrooms and lecture halls.

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.