Face Image Database Research Articles

1182 Ethnic Diversity in Facial Image Databases – the State of Play for Aesthetic Plastic Surgery

1182 Ethnic Diversity in Facial Image Databases – the State of Play for Aesthetic Plastic Surgery

An Ensemble Approach To Face Recognition In Access Control Systems

An Ensemble Approach To Face Recognition In Access Control Systems

Artificial intelligence facial recognition system for diagnosis of endocrine and metabolic syndromes based on a facial image database

AimTo build a facial image database and to explore the diagnostic efficacy and influencing factors of the artificial intelligence-based facial recognition (AI-FR) system for multiple endocrine and metabolic syndromes. MethodsIndividuals with multiple endocrine and metabolic syndromes and healthy controls were included from public literature and databases. In this facial image database, facial images and clinical data were collected for each participant and dFRI (disease facial recognition intensity) was calculated to quantify facial complexity of each syndrome. AI-FR diagnosis models were trained for each disease using three algorithms: support vector machine (SVM), principal component analysis k-nearest neighbor (PCA-KNN), and adaptive boosting (AdaBoost). Diagnostic performance was evaluated. Optimal efficacy was achieved as the best index among the three models. Effect factors of AI-FR diagnosis were explored with regression analysis. Results462 cases of 10 endocrine and metabolic syndromes and 2310 controls were included into the facial image database. The AI-FR diagnostic models showed diagnostic accuracies of 0.827–0.920 with SVM, 0.766–0.890 with PCA-KNN, and 0.818–0.935 with AdaBoost. Higher dFRI was associated with higher optimal area under the curve (AUC) (P = 0.035). No significant correlation was observed between the sample size of the training set and diagnostic performance. ConclusionsA multi-ethnic, multi-regional, and multi-disease facial database for 10 endocrine and metabolic syndromes was built. AI-FR models displayed ideal diagnostic performance. dFRI proved associated with the diagnostic performance, suggesting inherent facial features might contribute to the performance of AI-FR models.

Classification of stunted and normal children using novel facial image database and convolutional neural network

Malnutrition is a crucial problem that affects children’s development. Data released by UNICEF in 2022 shows that more than 7 million children under the age of 5 are still experiencing acute malnutrition in Ethiopia, Kenya, and Somalia. Meanwhile, in 2020, Indonesia ranked fifth and fourth highest in the world for wasting and stunting rates. The traditional approach to detect children’s nutritional status is by measuring the ratio between body weight and height at a certain age. The approach can be improved by simultaneously using facial biometrics, which can be accomplished automatically by employing facial recognition/classification based on computer vision and artificial intelligence methods. The goal of this research was to employ convolutional neural networks (CNN) as a method in the artificial intelligence field to classify children with malnutrition based on their face images. The method: a computer simulation of two CNN architectures applied to children’s facial image database. The simulation results were then evaluated to obtain the performance of the CNNs. The first task accomplished was to build a database of facial images of Indonesian children aged 2–5 years. The database comprises 4000 frontal facial images built from capturing images from 100 children, 50 normal/healthy, and 50 stunted children. In the database, some images were augmented using zoom-in, rotation, and shifting procedures. Using the database, we performed the second task by training two CNN architectures, AlexNet and ResNet34, to classify the images into normal children and children with malnutrition problems. We trained both architectures with 80% of the images and then validated and tested them with 10% of the images. Both architectures were learned with epochs: 20, 40, 60, 80, and 100, with a learning rate of 10-3. The models’ performances were shown in training, validation, and testing loss graphs and measured in accuracy, recall, precision, and F1 score. In conclusion, both architectures have shown promising results in classifying the images. Both architectures learned with epoch 60 rate 10-3 yielded the best models, with an accuracy of 0.9975 for AlexNet and 1 for ResNet34.

Artificial Intelligence Versus Human Focus Group Rating of Facial Attractiveness.

Background: Many open-access artificial intelligence (AI)-based websites that rate facial attractiveness are available, but none have been compared with human focus group outcomes. Objective: To compare human and AI-based websites scoring of facial attractiveness of adult female white faces. Methods: A 40-photograph database of AI-generated adult, white, female, expressionless, and frontal-view facial images were scored by otolaryngology residents and five AI-based facial rating websites: prettyscale.com, attractivenesstest.com, face-score.com/en, hotchat3000.com, and beautyscoretest.com. Sample t-test and bivariate correlation were performed for statistical analyses. Results: The focus group of 24 otolaryngology residents consisted of 62.5% males and 58.3% white participants. There was a strong positive correlation between average human score and average AI score for each photo (Pearson's correlation 0.84, p < 0.01). The average human raters' scores were significantly lower than the average AI scores (5.0 ± 1.8 vs. 6.9 ± 0.9, p < 0.01). Thirty images (75.0%) had statistically higher scores from the AI websites versus the focus group. On correlation analysis, all AI-based websites individually had scores that positively correlate with the human scores (all p < 0.05). Conclusion: AI-based websites and human focus-group scoring of facial attractiveness of adult white female faces were significantly correlated with the AI ratings biased toward higher values, encouraging their cautious utilization in future research.

Thermal-based gender recognition using drones: advancing biometric recognition in challenging outdoor environments

While biometric recognition typically uses features such as face, fingerprint, and iris to identify individuals, this study focuses on utilising specific characteristics to identify gender. The aim of this article is to propose a procedure for gender recognition under specific conditions. The specific condition addressed is outdoor area monitoring, which presents challenges such as varying lighting conditions and limited camera placement options. To tackle this, a proposed procedure utilises thermal images captured by the drone equipped with a thermal camera. The advantage of thermal images is their independence from ambient light conditions. The captured images are resized and processed using convolutional neural networks (CNNs) (AlexNet, VGG-16, VGG-19) for feature extraction and binary classification. A freely available database of thermal face images is used for training the CNNs, while a own created dataset of thermal images obtained by the drone is used for testing. The findings indicate that the optimised CNNs achieve classification accuracies of 82.4% (VGG-16), 82.9% (AlexNet), and 85.5% (VGG-19). The original contribution of this study lies in demonstrating the suitability of face thermal images obtained through drones for gender recognition purposes.

Contact-Free Atrial Fibrillation Screening With Attention Network.

Atrial Fibrillation (AF) screening from face videos has become popular with the trend of telemedicine and telehealth in recent years. In this study, the largest facial image database for camera-based AF detection is proposed. There are 657 participants from two clinical sites and each of them is recorded for about 10 minutes of video data, which can be further processed as over 10 000 segments around 30 seconds, where the duration setting is referred to the guideline of AF diagnosis. It is also worth noting that, 2 979 segments are segment-wise labeled, that is, every rhythm is independently labeled with AF or not. Besides, all labels are confirmed by the cardiologist manually. Various environments, talking, facial expressions, and head movements are involved in data collection, which meets the situations in practical usage. Specific to camera-based AF screening, a novel CNN-based architecture equipped with an attention mechanism is proposed. It is capable of fusing heartbeat consistency, heart rate variability derived from remote photoplethysmography, and motion features simultaneously to reliable outputs. With the proposed model, the performance of intra-database evaluation comes up to 96.62% of sensitivity, 90.61% of specificity, and 0.96 of AUC. Furthermore, to check the capability of adaptation of the proposed method thoroughly, the cross-database evaluation is also conducted, and the performance also reaches about 90% on average with the AUCs being over 0.94 in both clinical sites.

Face Recognition System Based Car Security System

Modern car security systems have been developed as a result of the growing demand in the automotive sector for increased convenience and security. In order to provide safe access management and authentication for cars, this paper suggests a face recognition system-based auto security system that makes use of facial recognition techniques. When a match is detected, the system uses computer vision algorithms to identify and detect the faces of approved drivers, enabling the car to be unlocked and started without any issues. In order to convert near infrared face images into their corresponding visible images, we develop a nir-vis image translation model. Using two distinct nir databases of face images, the pre-trained face embedding model is applied to present the evaluation performances. The proposed system offers improved security by eliminating the need for traditional key-based systems and providing a robust biometric-based authentication mechanism. Anti-spoofing procedures are also integrated to guarantee the system's dependability and defense against fraudulent attempts. One of the disadvantages of the current systems is that not everyone finds it comfortable to have to memorize their password for certain systems. An RFID card is another device that has been developed to verify the identification of the user. Hackers have the ability to modify RFID data and substitute it with their own. Certain systems utilize fingerprints to verify an individual's identity. The primary reasons for the low adoption of biometric fingerprint locks are their expensive cost and the potential for falsified or damaged fingerprints. Certain systems identify an obstruction ahead of the car, sound an alert, tell the driver to pull over, but do nothing to actually avoid the obstruction. Through tests and assessments, the usefulness and practicability of the suggested system are shown, highlighting its potential as a workable option for safe and practical access management in automotive settings

Advanced Deep Learning Techniques for High-Quality Synthetic Thermal Image Generation

In this paper, we introduce a cutting-edge system that leverages state-of-the-art deep learning methodologies to generate high-quality synthetic thermal face images. Our unique approach integrates a thermally fine-tuned Stable Diffusion Model with a Vision Transformer (ViT) classifier, augmented by a Prompt Designer and Prompt Database for precise image generation control. Through rigorous testing across various scenarios, the system demonstrates its capability in producing accurate and superior-quality thermal images. A key contribution of our work is the development of a synthetic thermal face image database, offering practical utility for training thermal detection models. The efficacy of our synthetic images was validated using a facial detection model, achieving results comparable to real thermal face images. Specifically, a detector fine-tuned with real thermal images achieved a 97% accuracy rate when tested with our synthetic images, while a detector trained exclusively on our synthetic data achieved an accuracy of 98%. This research marks a significant advancement in thermal image synthesis, paving the way for its broader application in diverse real-world scenarios.

Giant panda face recognition based on PandaFaceNet

Individual recognition of animals via infrared camera trapping surveys is an important method for protecting and monitoring animals in the wild. However, several factors limit current survey methods used for individual animal recognition, such as the lack of accuracy and extensive time required to process data. Recently, new technologies and methods for individual recognition of animal images have been developed for rare wildlife species (e.g., giant pandas and lemurs). These new technologies require adequate and high-quality sampled images; however, it can be challenging for researchers to obtain an adequate sample size of wildlife images from the field. To overcome this problem, we proposed and tested a new small-sample individual recognition method adapted from FaceNet called PandaFaceNet, using data from a self-built giant panda (Ailuropoda melanoleuca) facial image database. We tested the proposed giant panda individual recognition method on unknown captive and wild giant panda datasets. The results showed that this method has 95.3% recognition accuracy for distinguishing among two captive giant panda facial images and 91% recognition accuracy for distinguishing among two wild giant pandas. Notably, PandaFaceNet achieves individual recognition through comparing two images and is an open-set identification method. Therefore, PandaFaceNet provides a novel method for giant panda research by opening up opportunities for analysis of small sample sizes of panda imagery data, while also providing new directions for research on rare wildlife more broadly.

Efficient Privacy-Preserving Face Identification Protocol

Face identification is an important research topic in artificial intelligence and it has wide application in civilian and law enforcement scenarios that require reliable biometric identification. However, the wide deployment of face identification systems has raised serious privacy concerns about the abuse of users' face images. In this paper, we propose an efficient privacy-preserving face identification protocol based on fully homomorphic encryption and garbled circuit. Our work is the first privacy-preserving face identification protocol that guarantees the privacy of the users' face images such that the server cannot learn any information about the face images, access pattern and identification result, while providing face identification service. We formally prove the security of our protocol in a semi-honest model. Experimental results the practicality of our protocol for the large face images database.

Facial morphometric differences across face databases: influence of ethnicities and sex.

The scientific need for standardized, high-quality facial stimuli has driven the creation of several face image databases in recent years. These stimuli are particularly important in facial asymmetry research. However, previous studies have reported facial anthropometric differences across a variety of ethnicities. This highlights the need to investigate whether these differences can also impact the use of face image databases, particularly in facial asymmetry research. In this study, we investigated facial asymmetry-based morphometric differences between the multi-ethnic Chicago Face Database (CFD) and the LACOP Face Database, which is composed of Brazilian subjects. We found reliable differences in facial asymmetry between the two databases, which were related to ethnic groups. Specifically, differences in eye and mouth asymmetry seem to drive these differences. The asymmetry-based morphometric differences among databases and ethnicities found in this study reinforce the necessity of creating multi-ethnic face databases.

Truth-value unconstrained face clustering for identity resolution in a distributed environment of criminal police information systems

The work of criminal police in contemporary society is characterized by the proliferation of data and information to be processed, a more significant limitation of access to personal data, increased public monitoring, and higher expectations in the efficiency of identifying perpetrators. Also, all information collected during investigations is distributed through police systems separated by different levels and organization units, and often there are insufficient human and material resources. Resolving the perpetrator’s identity in those circumstances is a complex task, and police decision support systems must group all available evidence related to specific persons. For that purpose, this paper proposes a new approach to unconstrained pairwise clustering of face feature vectors extracted from the histogram of oriented gradients descriptor, named Truth-value clustering (TVC), based on non-axiomatic logic and graphs. The clustering approach was experimentally tested with six different face image databases. They were created to simulate unconstrained conditions like IARPA Janus Benchmark-B Face Dataset (IJB-B), IMDb-Wiki, Labeled Faces in the Wild, and YouTube Faces. The results of the proposed solution are compared with other state-of-the-art methods, showing that the approach gives, in summary, significantly better results. Application of the IJB-B protocol created for testing face clustering showed that the new approach gives better results by an average of 8.25% (σ=4.2). The main advantage over other methods is the possibility of utilizing mechanisms from non-axiomatic logic such as revision, which can then acquire new knowledge based on information from different nodes of the distributed environment consisting of various police information systems.

Finding Missing Person Based on Face Recognition Using AI in Video Surveillance System

Abstract: AI-based face recognition for missing person searches is a promising strategy that has the potential to significantly speed up and improve accuracy. The system makes use of artificial intelligence algorithms to match surveillance camera realtime video footage with facial images of people who have gone missing. Face recognition technology in video surveillance systems is used in this project to come up with a strategy for locating people who have gone missing. The system involves gathering information about the missing person, creating a database of facial images, and matching those images to real-time video footage with the help of artificial intelligence algorithms. The goal of the field of computer science known as artificial intelligence (AI) is to create intelligent machines that can carry out tasks that typically call for human intelligence. This includes tasks like translating languages, visual perception, speech recognition, and decision-making. Machine learning algorithms are used by AI systems to learn from data and allow them to improve their performance over time. A powerful method for training artificial neural networks with many layers, deep learning, a subset of machine learning, has emerged, allowing. The system can be used to quickly identify and locate missing people in public places like airports and train stations. The speed and accuracy of missing person searches could be significantly enhanced by the proposed system, increasing the likelihood of successful reunions. Finding missing people in view of face acknowledgment utilizing Convolutional Brain Organization (CNN) calculation is a well-known approach that has shown promising outcomes. CNN is a deep learning algorithm that works well for face recognition because it is widely used for image recognition and classification.

An Analysis of Global and Adaptive Thresholding for Biometric Images Based on Neutrosophic Overset and Underset Approaches

The study introduces a new threshold method based on a neutrosophic set. The proposal applies the neutrosophic overset and underset concepts for thresholding the image. The global threshold method and the adaptive threshold method were used as the two types of thresholding methods in this article. Images could be symmetrical or asymmetrical in professional disciplines; the government maintains facial image databases as symmetrical. General-purpose images do not need to be symmetrical. Therefore, it is essential to know how thresholding functions in both scenarios. Since the article focuses on biometric image data, face and fingerprint data were considered for the analysis. The proposal provides six techniques for the global threshold method based on neutrosophic membership, indicating neutrosophic TF overset (NOTF), neutrosophic TI overset (NOTI), neutrosophic TIF overset (NOTIF), neutrosophic TF underset (NUTF), neutrosophic TI underset (NUTI), neutrosophic TIF underset (NUTIF); similarly, in this study, the researchers generated six novel approaches for the adaptive method. These techniques involved an investigation using biometric data, such as fingerprints and facial images. The achievement was 98% accurate for facial image data and 100% accurate for fingerprint data.

A robust mixed error coding method based on nonconvex sparse representation

Linear representation based methods have been extensively applied in image recognition, especially for those with noise, illumination changes, and occlusions. However, most existing methods assume a specific distribution for image noise estimation, which is intractable to handle complex variations. Besides, they usually use convex norm to describe the noise sparse and low-rank property, and it is a biased approximation. To address these problems, we propose a novel nonconvex regularized robust mixed error coding (NRRM) method, which uses mixed norms from both 1D and 2D perspectives to model the complex image noise without convex relaxation. In specific, we use weighted ℓ2-norm based robust coding to characterize the sparse noise in images, and weighted matrix nuclear norm to characterize the low-rank noise. Compared with traditional regression approaches, our method can more fine-grained and accurate to capture noise and alleviate its negative influence for robust recognition. Besides, we constrain the representation component in a group-wise manner to weigh the roles of different classes. The NRRM model is solved efficiently by adopting an alternating direction method of multipliers (ADMM) algorithm. Comprehensive experiments on some benchmark face image databases validate the superiority of NRRM over several state-of-the-art linear representation based methods.

Locality-Sensitive Hashing of Soft Biometrics for Efficient Face Image Database Search and Retrieval

As multimedia technology has advanced in recent years, the use of enormous image libraries has dramatically expanded. In applications for image processing, image retrieval has emerged as a crucial technique. Content-based face image retrieval is a well-established technology in many real-world applications, such as social media, where dependable retrieval capabilities are required to enable quick search among large numbers of images. Humans frequently use faces to recognize and identify individuals. Face recognition from official or personal photos is becoming increasingly popular as it can aid crime detectives in identifying victims and criminals. Furthermore, a large number of images requires a large amount of storage, and the process of image comparison and matching, consequently, takes longer. Hence, the query speed and low storage consumption of hash-based image retrieval techniques have garnered a considerable amount of interest. The main contribution of this work is to try to overcome the challenge of performance improvement in image retrieval by using locality-sensitive hashing (LSH) for retrieving top-matched face images from large-scale databases. We use face soft biometrics as a search input and propose an effective LSH-based method to replace standard face soft biometrics with their corresponding hash codes for searching a large-scale face database and retrieving the top-k of the matching face images with higher accuracy in less time. The experimental results, using the Labeled Faces in the Wild (LFW) database together with the corresponding database of attributes (LFW-attributes), show that our proposed method using LSH face soft biometrics (Soft BioHash) improves the performance of face image database search and retrieval and also outperforms the LSH hard face biometrics method (Hard BioHash).

FaceIDP: Face Identification Differential Privacy via Dictionary Learning Neural Networks

In big-data era, large amount of facial images could be used to breach the face identification system, which demands effective Face IDentification Differential Privacy (FaceIDP) of the facial images for widespread adoption of the face identification technique. In this paper, to our best knowledge, we take the first step to systematically study an effective important FaceIDP approach via the help of Dictionary Learning (DL) for secure releasing of facial images. First, a Dictionary Learning neural Network (DLNet) has been developed and trained with the facial images database, to learn the common dictionary basis of the facial image database. Then, the coding coefficients of the facial images are obtained. After that, the sanitizing noise is added to the coding coefficients, which obfuscates the facial feature vector that is used to identify a user’s identification. We have also proved that the FaceIDP is ε-differentially private. More importantly, optimal noise scale parameters have been obtained via the Lagrange Multiplier (LM) method to achieve better data utility for a given privacy budget ε. Finally, substantial experiments have been conducted to validate the efficiency of the FaceIDP with two real-life facial image databases.

Study of input images parameters for improving person identification information technology

This article describes the research of the algorithm based on local-textural descriptors, which is the basis of the information technology of person identification, with the aim of increasing its efficiency and reducing the probability of identification errors. An analysis of previous studies has shown that the performance of a face recognition and identification algorithm can vary significantly after applying it to images from different datasets. In addition, the issue of using local texture descriptors in the tasks of face recognition and identification on images of different quality has not been sufficiently studied in modern research. Therefore, the main goal of this work is to study the parameters of face images that will be used for identification process with the appliance of the algorithm based on such local-textural methods as histograms of oriented gradients and local binary patterns in one-dimensional space. During the experiments, it was established that the algorithm identification accuracy rate can be decreased by 35-70% when it is applied to image samples taken in unconstrained conditions. In order to investigate the possibility of reducing the variation of the results, experiments were conducted on the same images with the transformation of their properties, such as format and resolution. During the research, experiments were carried out with several of the most common databases of face images, as a result of which the efficiency of the algorithm reached the level of accuracy of identification of 95% on images taken under controlled conditions and unified within one database. As a result, it was found that in some cases, the conversion of the image format submitted to the input of the algorithm can increase the accuracy of the algorithm’s identification by 5%. Also, the efficiency of the algorithm is affected by the resolution of the input images, in particular, due to the application of the algorithm to transformed images from the Database of Faces database, the accuracy of identification increased by 5-10%, FERET — decreased by 5-35%, SCface — decreased by 10-30%, CFP — increased by 5-10%, Tinyface — increased by 25-35%, LFW — increased by 15%, AgeDB — increased by 5%. The highest identification accuracy rate of the algorithm, which is 95%, was obtained after its application to images taken under constrained conditions and unified within the same database.

Fun Selfie Filters in Face Recognition: Impact Assessment and Removal

This work investigates the impact of fun selfie filters, which are frequently used to modify selfies, on face recognition systems. Based on a qualitative assessment and classification of freely available mobile applications, ten relevant fun selfie filters are selected to create a database. To this end, the selected filters are automatically applied to face images of public face image databases. Different state-of-the-art methods are used to evaluate the influence of fun selfie filters on the performance of face detection using dlib, RetinaFace, and a COTS method, sample quality estimated by FaceQNet and MagFace, and recognition accuracy employing ArcFace and a COTS algorithm. The obtained results indicate that selfie filters negatively affect face recognition modules, especially if fun selfie filters cover a large region of the face, where the mouth, nose, and eyes are covered. To mitigate such unwanted effects, a GAN-based selfie filter removal algorithm is proposed which consists of a segmentation module, a perceptual network, and a generation module. In a cross-database experiment the application of the presented selfie filter removal technique has shown to significantly improve the biometric performance of the underlying face recognition systems.