Digital Face Images Research Articles

Disappearance of the face: From early photography to facial recognition systems

In this article I analyse the hidden genealogical link between portrait photography, used for criminological and psychiatric purposes, and contemporary systems of biometric identification of the human face. The aim is to highlight the shift between the emphasis on the importance of the human ‘expert eye’ in recognizing the face when talking about nineteenth-century photography and the use of computer technology that produces and reads digital facial images. In both cases, however, these are modes and variants of reducing and flattening the human face; the face itself disappears under the onslaught of technologies of vision and mediation, becoming a mere data set. Special attention is devoted to the pose, the frontal view, which is the technological a priori of the empirical possibility of recognition, articulated through the history of visualization of the face from early portrait photography to facial recognition systems. Consequently, what we call the ‘face’ is a simulacra of individual the face presented to the apparatus: the ‘real’ face is transposed upon multiple technological layers (camera – plates – photographic surface), losing its characteristic features to be re-written according to specific techniques of measurement.

A Preliminary Evaluation of the Diagnostic Performance of a Smartphone-Based Machine Learning-Assisted System for Evaluation of Clinical Activity Score in Digital Images of Thyroid-Associated Orbitopathy.

Background: We previously developed a machine learning (ML)-assisted system for predicting the clinical activity score (CAS) in thyroid-associated orbitopathy (TAO) using digital facial images taken by a digital single-lens reflex camera in a studio setting. In this study, we aimed to apply this system to smartphones and detect active TAO (CAS ≥3) using facial images captured by smartphone cameras. We evaluated the performance of our system on various smartphone models and compared it with the performance of ophthalmologists with varying clinical experience. Methods: We applied the preexisting ML architecture to classify photos taken with smartphones (Galaxy S21 Ultra, iPhone 12 pro, iPhone 11, iPhone SE 2020, Galaxy M20, and Galaxy A21S). The performance was evaluated with smartphone-captured images from 100 patients with TAO. Three ophthalmology residents, three general ophthalmologists with <5 years of clinical experience, and three oculoplastic specialists independently interpreted the same set of images taken under a studio environment and compared their results with those generated by the smartphone-based ML-assisted system. Reference CAS was determined by a consensus of three oculoplastic specialists. Results: Active TAO (CAS ≥3) was identified in 28 patients. Smartphone model used in capturing facial images influenced active TAO detection performance (F1 score 0.59-0.72). The smartphone-based system showed 74.5% sensitivity, 84.8% specificity, and F1 score 0.70 on top three smartphones. On images from all six smartphones, average sensitivity, specificity, and F1 score were 71.4%, 81.6%, and 0.66, respectively. Ophthalmology residents' values were 69.1%, 55.1%, and 0.46. General ophthalmologists' values were 61.9%, 79.6%, and 0.55. Oculoplastic specialists' values were 73.8%, 90.7%, and 0.75. This smartphone-based ML-assisted system predicted CAS within 1 point of reference CAS in 90.7% using facial images from smartphones. Conclusions: Our smartphone-based ML-assisted system shows reasonable accuracy in detecting active TAO, comparable with oculoplastic specialists and outperforming residents and general ophthalmologists. It may enable reliable self-monitoring for disease activity, but confirmatory research is needed for clinical application.

Operator dependence of methods for obtaining metric characteristics of a face with real measurements and digital images

BACKGROUND: No studies in the scientific literature have established the operator dependence of portrait identification with the manual method of measuring facial parameters in subjects compared with measurements from digital images. The labor costs and accuracy of the measurements with these methods have not been established.&#x0D; AIM: To establish the operator dependence of methods of real-face measurement and measurement on digital images.&#x0D; MATERIALS AND METHODS: Facial parameters were instrumentally measured in 24 Caucasian women aged 19–20 years who were studying at the Professor V.F. Voino-Yasenetsky Krasnoyarsk State Medical University by four researchers independently of each other. The same type of standard digital photography of each subject’s face was taken in five projections, followed by a comparison of the results of real measurements with digital ones.&#x0D; RESULTS: When comparing the results of facial measurements obtained from digital images and manual method, an error was observed both in the measurements of one parameter by one researcher using two methods and in the excellent results obtained by other researchers. However, the greatest deviations were observed with the manual method, which may be due to the dependence of measurements on the operator.&#x0D; CONCLUSION: With a streamlined and methodically verified approach to working with digital facial images, this technique is more accurate and less labor-intensive than real measurements because of the absence of operator dependence, which can be used in investigating crimes.

A Review of Face Detection Methods Based on Feature Approach

Face detection technology is the first and essential step for facial-based analysis algorithms such as face recognition, face feature extraction, face alignment, face enhancement, and face parsing. That is besides serving other applications related to human intention and act analysis such as facial expression recognition, gender recognition, and age classification. Face detection is used to detect faces in digital images. It is a special case of object detection and can be used in many areas such as biometrics, security, law enforcement, entertainment, and personal safety. There are various methods proposed in the field of face detection, and they all compete to make it more advanced and accurate. These methods belong to two main approaches; feature-based approach and image-based approach. This paper reviews the face detection methods that belong to the feature-based approach. Moreover, their work concepts, strengths, and limitations are mentioned. This paper concentrates on the feature-based approach due to its simplicity and high applicability in real-time detection compared to the image-based approach. Index Terms— Face Detection Methods, Feature-Based Approach, Viola-Jones Algorithm, Deformable Template Matching.

A novel blind tamper detection and localization scheme for multiple faces in digital images

AbstractFace image manipulation detection (FIMD) is a research area of great interest, widely applicable in fields requiring data security and authentication. Existing FIMD schemes aim to identify manipulations in digital face images, but they possess individual strengths and limitations. Most schemes can only detect specific manipulations under certain conditions, leading to variable success rates across different images. The literature lacks emphasis on detecting manipulations involving multiple faces. This paper introduces a novel blind tamper detection and localization scheme specifically designed for multiple faces in digital images. The proposed multiple faces manipulation detection (MFMD) scheme consists of two stages: face detection and selection, and image watermarking. Through extensive experiments, the MFMD scheme's performance has been evaluated on various multiple‐face images, considering embedding capacity, payload, watermarked image quality, time complexity, and manipulation detection ability. The results demonstrate the MFMD scheme's efficacy in detecting different types of manipulations for multiple faces in images. Furthermore, the watermarked images exhibit high visual quality, even when multiple faces are present. The scheme's efficiency recommends it for practical applications, especially in sharing personal images over unsecured networks. This research advances FIMD techniques by addressing the neglected area of multiple‐face manipulation detection. With improved accuracy, faster processing times, and resilience against various manipulations, the MFMD scheme offers valuable capabilities for enhancing data security and authentication in real‐world scenarios.

Combined genome-wide association study of 136 quantitative ear morphology traits in multiple populations reveal 8 novel loci.

Human ear morphology, a complex anatomical structure represented by a multidimensional set of correlated and heritable phenotypes, has a poorly understood genetic architecture. In this study, we quantitatively assessed 136 ear morphology traits using deep learning analysis of digital face images in 14,921 individuals from five different cohorts in Europe, Asia, and Latin America. Through GWAS meta-analysis and C-GWASs, a recently introduced method to effectively combine GWASs of many traits, we identified 16 genetic loci involved in various ear phenotypes, eight of which have not been previously associated with human ear features. Our findings suggest that ear morphology shares genetic determinants with other surface ectoderm-derived traits such as facial variation, mono eyebrow, and male pattern baldness. Our results enhance the genetic understanding of human ear morphology and shed light on the shared genetic contributors of different surface ectoderm-derived phenotypes. Additionally, gene editing experiments in mice have demonstrated that knocking out the newly ear-associated gene (Intu) and a previously ear-associated gene (Tbx15) causes deviating mouse ear morphology.

A novel hybrid face mask detection approach using Transformer and convolutional neural network models.

Face and face mask detection are one of the most popular topics in computer vision literature. Face mask detection refers to the detection of people's faces in digital images and determining whether they are wearing a face mask. It can be of great benefit in different domains by ensuring public safety through the monitoring of face masks. Current research details a range of proposed face mask detection models, but most of them are mainly based on convolutional neural network models. These models have some drawbacks, such as their not being robust enough for low quality images and their being unable to capture long-range dependencies. These shortcomings can be overcome using transformer neural networks. Transformer is a type of deep learning that is based on the self-attention mechanism, and its strong capabilities have attracted the attention of computer vision researchers who apply this advanced neural network architecture to visual data as it can handle long-range dependencies between input sequence elements. In this study, we developed an automatic hybrid face mask detection model that is a combination of a transformer neural network and a convolutional neural network models which can be used to detect and determine whether people are wearing face masks. The proposed hybrid model's performance was evaluated and compared to other state-of-the-art face mask detection models, and the experimental results proved the proposed model's ability to achieve a highest average precision of 89.4% with an execution time of 2.8 s. Thus, the proposed hybrid model is fit for a practical, real-time trial and can contribute towards public healthcare in terms of infectious disease control.

Comparison of the accuracy of different handheld-type scanners in three-dimensional facial image recognition.

Handheld-type scanners are widely used in clinical practice. This study examined the accuracy of handheld-type scanners using plaster statues to assess their performance in facial recognition. Twelve 4-mm zirconia balls as measuring points were attached to the facial portions of three types of plaster statue. Six digital facial images of each plaster statue were obtained using one of the following five handheld-type scanners: Artec Eva, Artec Spider, Bellus 3D FaceApp, SNAP, and Vectra H1. Four-millimeter spherical objects were manually placed at the measurement points on the scanned data generated using computer-aided design software and coordinate positions were measured using a contact-type high-resolution three-dimensional measurement device. Consequently, the discrepancy between the distance measured using the contact-type device and that measured using the handheld-type scanner was calculated. The scanning time, processing time, and deviation of the distance between the measuring points were analyzed using two-way analysis of variance and t-test with Bonferroni correction. The scanning and processing times ranged from 15.2 to 42.2 s and 20.7 to 234.2 s, respectively. Overall, 97% of all measured distances by Spider were within ±1.00% deviation; 79%, Vectra; 73%, Eva; 70%, Bellus; and 42%, SNAP. The performance of handheld-type scanners using plaster statues varied among the different scanners. The scanning time of Eva and the processing time of Bellus were significantly shorter than those of other scanners. Furthermore, Spider exhibited the best accuracy, followed by Eva, Vectra, Bellus, and SNAP.

Machine learning-assisted system using digital facial images to predict the clinical activity score in thyroid-associated orbitopathy

Although the clinical activity score (CAS) is a validated scoring system for identifying disease activity of thyroid-associated orbitopathy (TAO), it may produce differing results depending on the evaluator, and an experienced ophthalmologist is required for accurate evaluation. In this study, we developed a machine learning (ML)-assisted system to mimic an expert’s CAS assessment using digital facial images and evaluated its accuracy for predicting the CAS and diagnosing active TAO (CAS ≥ 3). An ML-assisted system was designed to assess five CAS components related to inflammatory signs (redness of the eyelids, redness of the conjunctiva, swelling of the eyelids, inflammation of the caruncle and/or plica, and conjunctival edema) in patients’ facial images and to predict the CAS by considering two components of subjective symptoms (spontaneous retrobulbar pain and pain on gaze). To train and test the system, 3,060 cropped images from 1020 digital facial images of TAO patients were used. The reference CAS for each image was scored by three ophthalmologists, each with > 15 years of clinical experience. We repeated the experiments for 30 randomly split training and test sets at a ratio of 8:2. The sensitivity and specificity of the ML-assisted system for diagnosing active TAO were 72.7% and 83.2% in the test set constructed from the entire dataset. For the test set constructed from the dataset with consistent results for the three ophthalmologists, the sensitivity and specificity for diagnosing active TAO were 88.1% and 86.9%. In the test sets from the entire dataset and from the dataset with consistent results, 40.0% and 49.9% of the predicted CAS values were the same as the reference CAS, respectively. The system predicted the CAS within 1 point of the reference CAS in 84.6% and 89.0% of cases when tested using the entire dataset and in the dataset with consistent results, respectively. An ML-assisted system estimated the clinical activity of TAO and detect inflammatory active TAO with reasonable accuracy. The accuracy could be improved further by obtaining more data. This ML-assisted system can help evaluate the disease activity consistently as well as accurately and enable the early diagnosis and timely treatment of active TAO.

A New Design of Occlusion-Invariant Face Recognition Using Optimal Pattern Extraction and CNN with GRU-Based Architecture

Face detection is a computer technology being used in a variety of applications that identify human faces in digital images. In many face recognition challenges, Convolutional Neural Networks (CNNs) are regarded as a problem solver. Occlusion is determined as the most common challenge of face recognition in realistic applications. Several studies are undergoing to obtain face recognition without any challenges. However, the occurrence of noise and occlusion in the image reduces the achievement of face recognition. Hence, various researches and studies are carried out to solve the challenges involved with the occurrence of occlusion and noise in the image, and more clarification is needed to acquire high accuracy. Hence, a deep learning model is intended to be developed in this paper using the meta-heuristic approach. The proposed model covers four main steps: (a) data acquisition, (b) pre-processing, (c) pattern extraction and (d) classification. The benchmark datasets regarding the face image with occlusion are gathered from a public source. Further, the pre-processing of the images is performed by contrast enhancement and Gabor filtering. With these pre-processed images, pattern extraction is done by the optimal local mesh ternary pattern. Here, the hybrid Whale–Galactic Swarm Optimization (WGSO) algorithm is used for developing the optimal local mesh ternary pattern extraction. By inputting the pattern-extracted image, the new deep learning model namely “CNN with Gated Recurrent Unit (GRU)” network performs the recognition process to maximize the accuracy, and also it is used to enhance the face recognition model. From the results, in terms of accuracy, the proposed WGSO-[Formula: see text] model is better by 4.02%, 3.76% and 2.17% than the CNN, SVM and SRC, respectively. The experimental results are presented by performing their comparative analysis on a standard dataset, and they assure the efficiency of the proposed model. However, many challenging problems related to face recognition still exist, which offer excellent opportunities to face recognition researchers in the future.

Hybrid Facial Expression Recognition (FER2013) Model for Real-Time Emotion Classification and Prediction

Facial expression recognition is a vital research topic in most fields ranging from artificial intelligence and gaming to human–computer interaction (HCI) and psychology. This paper proposes a hybrid model for facial expression recognition, which comprises a deep convolutional neural network (DCNN) and a Haar Cascade deep learning architecture. The objective is to classify real-time and digital facial images into one of the seven facial emotion categories considered. The DCNN employed in this research has more convolutional layers, ReLU activation functions, and multiple kernels to enhance filtering depth and facial feature extraction. In addition, a Haar Cascade model was also mutually used to detect facial features in real-time images and video frames. Grayscale images from the Kaggle repository (FER-2013) and then exploited graphics processing unit (GPU) computation to expedite the training and validation process. Pre-processing and data augmentation techniques are applied to improve training efficiency and classification performance. The experimental results show a significantly improved classification performance compared to state-of-the-art (SoTA) experiments and research. Also, compared to other conventional models, this paper validates that the proposed architecture is superior in classification performance with an improvement of up to 6%, totaling up to 70% accuracy, and with less execution time of 2,098.8 s.

Hybrid facial expression recognition (FER2013) modelfor real-time emotion classification and prediction

Facial expression recognition is a vital research topic in most fields ranging from artificial intelligence and gamingto human-computer interaction (HCI) and psychology. This paper proposes a hybrid model for facial expressionrecognition, which comprises a deep convolutional neural network (DCNN) and a Haar Cascade deep learningarchitecture. The objective is to classify real-time and digital facial images into one of the seven facial emotioncategories considered. The DCNN employed in this research has more convolutional layers, ReLU activationfunctions, and multiple kernels to enhance filtering depth and facial feature extraction. In addition, a HaarCascade model was also mutually used to detect facial features in real-time images and video frames. Grayscaleimages from the Kaggle repository (FER2013) and then exploited graphics processing unit (GPU) computation toexpedite the training and validation process. Pre-processing and data augmentation techniques are applied toimprove training efficiency and classification performance. The experimental results show a significantly improvedclassification performance compared to state-of-the-art (SoTA) experiments and research. Also, compared to otherconventional models, this paper validates that the proposed architecture is superior in classification performancewith an improvement of up to 6%, totaling up to 70% accuracy, and with less execution time of 2,098.8 s

Machine Learning-Assisted System Using Digital Facial Images to Predict the Clinical Activity Score in Thyroid-Associated Orbitopathy

Machine Learning-Assisted System Using Digital Facial Images to Predict the Clinical Activity Score in Thyroid-Associated Orbitopathy

Levenberg-marquardt backpropagation neural network with techebycheve moments for face detection

Face detection is an intelligent approach used in a variety of applications that identifies human faces in digital images. This work presents a new method which composes of a neural network and Techebycheve transforms for face detection. For feature extraction, Tchebychev transform was applied, in which a discrete Tchebychev transform is given for different sampling patterns and several samples here were performed on color images. A Levenberg-Marquardt backpropagation neural network was applied to the transformed image to find faces in the face detection dataset and FDDB benchmarked database. Model performance was measured based on its accuracy and the best result from the newly proposed method was 98.9%. Simulation results showed that the proposed method handles face detection efficiently.

Security Implementation by Machine Learning Based Face Recognization with SVM

Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images. Face detection also refers to the psychological process by which humans locate and attend to faces in a visual scene. Face detection can be regarded as a specific case of object-class detection. In object-class detection, the task is to find the locations and sizes of all objects in an image that belong to a given. As per the various face detection system seen various work done onto the detection with various way. In existing this are get evaluate with the HOG with SVM, which will help us to get the exact value so that it is necessary to implement the system which will more effective and advance. As per the face detection seen there are various face detection systems are implemented. Determining face is easy but recognition is quite typical so that we are proposed machine learning based face recognition with SVM which helps to determine and detect the faces So the proposed system will get integrated with highly efficient and effective SVM model for face recognition. The proposed methodology will help us to implement the face based security implementation in any security system like door lock, mobile screen lock etc.

Security Implementation by Machine Learning Based Face Recognization with SVM

Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images. Face detection also refers to the psychological process by which humans locate and attend to faces in a visual scene. Face detection can be regarded as a specific case of object-class detection. In object-class detection, the task is to find the locations and sizes of all objects in an image that belong to a given. As per the various face detection system seen various work done onto the detection with various way. In existing this are get evaluate with the HOG with SVM, which will help us to get the exact value so that it is necessary to implement the system which will more effective and advance. As per the face detection seen there are various face detection systems are implemented. Determining face is easy but recognition is quite typical so that we are proposed machine learning based face recognition with SVM which helps to determine and detect the faces So the proposed system will get integrated with highly efficient and effective SVM model for face recognition. The proposed methodology will help us to implement the face based security implementation in any security system like door lock, mobile screen lock etc.

Effects of image compression on face image manipulation detection: A case study on facial retouching

Numerous methods have been introduced to reliably detect digital face image manipulations in the past. Of late, the generalisability of these schemes has been questioned in particular with respect to image post-processing. Image compression represents a post-processing which is frequently applied in diverse biometric application scenarios. Severe compression might erase digital traces of face image manipulation and hence hamper a reliable detection thereof. In this work, the effects of image compression on the face image manipulation detection are analysed. In particular, a case study on facial retouching detection under the influence of image compression is presented. To this end, ICAO-compliant subsets of two public face databases are used to automatically create a database containing more than 9000 retouched reference images together with unconstrained probe images. Subsequently, reference images are compressed applying JPEG and JPEG 2000 at compression levels recommended for face image storage in electronic travel documents. Novel detection algorithms utilising texture descriptors and deep face representations are proposed and evaluated in a single image and differential scenario. Results obtained from challenging cross-database experiments in which the analysed retouching technique is unknown during training yield interesting findings: (1) most competitive detection performance is achieved for differential scenarios employing deep face representations; (2) image compression severely impacts the performance of face image manipulation detection schemes based on texture descriptors while methods utilising deep face representations are found to be highly robust; (3) in some cases, the application of image compression might as well improve detection performance.

Enhanced AdaBoost Haar Cascade Classifier Model to Detect Partially Occluded Faces in Digital Images

Enhanced AdaBoost Haar Cascade Classifier Model to Detect Partially Occluded Faces in Digital Images

One-Year Follow-Up Study of Changes in Prostaglandin-Associated Periorbital Syndrome After Switch From Conventional Prostaglandin F2alfa to Omidenepag Isopropyl.

BackgroundCosmetic problems induced by conventional prostaglandin F2α (PGF2α) analogs are common. We prospectively evaluated the improvement of patients with prostaglandin-associated periorbital syndrome (PAPS) for whom the treatment regimen was switched from conventional PGF2α analogs to a new selective prostaglandin-EP2 agonist (i.e., omidenepag isopropyl).MethodsWe finally evaluated 12 patients with follow-up for one year who changed the therapy from conventional PGF2α drugs to omidenepag isopropyl. Digital facial images of the patients were captured prior to the initiation of therapy with omidenepag isopropyl and after approximately three, six, and 12 months. Three independent observers judged the recovery according to the five signs of PAPS - deepening of the upper eyelid sulcus (DUES), flattening of the lower eyelid bags, upper eyelid ptosis, ciliary hypertrichosis, and periorbital skin hyperpigmentation - by comparing images at baseline and each month.ResultsThe mean age of patients (eight females; four males) was 61 years. The original PGF2α drugs were bimatoprost (N = 7), latanoprost (N = 3), travoprost (N = 1), and tafluprost (N = 1). The mean duration of treatment with PGF2α was 61 months. PAPS signs were evaluated in 11 patients after three months and in all 12 patients after six and 12 months. After three, six, and 12 months, DUES improved in five, six, and six patients, respectively; flattening of the lower eyelid bags improved in two, two, and three patients, respectively; upper eyelid ptosis improved in zero, one, and two patients, respectively; ciliary hypertrichosis improved in zero, one, and zero patients, respectively; and eyelid pigmentation improved in one, five, and three patients, respectively. Recovery of DUES was the most observed sign at ≤50%, whereas the recovery of ciliary hypertrichosis was the least sign at ≤8% at 12 months. All patients with improved DUES at one year had been receiving bimatoprost or travoprost.ConclusionsSome PAPS signs improved after the administration of omidenepag isopropyl for one year. Our findings are useful for patients suffering from cosmetic problems induced by conventional PGF2α analogs.

Face Detection Based On Eye-Mouth Triangular Approach

Human face is a complex and dynamic structure. It is a challenge to be able to make a face recognition system like humans. At the beginning of its development, many facial recognition studies only focused on facial features. In 1991, Turk and Pentland developed a face recognition system based on Principal Component Analysis named eigenface. This system is very efficient because it only focuses on components that most affect facial image. However, this system has weaknesses, which cannot be used to determine the position of the face. In this final project, image processing methods will be carried out to detect faces in digital images. The method used is eye mouth triangular approach with the steps being taken are skin detection, eye detection, mouth detection, and facial confirmation. From the results of a hundred digital color images tested, there were 82 images that were successfully detected. The main system failure is caused by failure in skin detection. Further development is needed so that the system can work optimally.