Features Of Face Images Research Articles

Research on Artificial Intelligence and Face Recognition

With the rapid progress of artificial intelligence technologies, face recognition has emerged as a critical domain within biometrics. The primary objective of face recognition technology is to facilitate rapid identification and authentication of individuals by analyzing facial image features. This technology has been widely applied in various sectors, including security surveillance, financial transactions, education, healthcare, and more. This paper presents a comprehensive overview and analysis of contemporary face recognition methodologies, including geometric feature extraction, the combination of Wavelet transform and Principal Component Analysis (PCA) in artificial neural networks, elastic graph matching, Linear Discriminant Hashing (LHD), and Support Vector Machines (SVM). Through a comparative evaluation of these methods, this study clarifies their respective advantages and limitations. The findings indicate that face recognition technology basically consists of essential components such as face detection, image processing, feature extraction, feature comparison, and decision-making for recognition. Although there are similarities in the general workflow among different methodologies, each approach optimizes specific aspects to varying extents. As a result, every method has its own distinct strengths and weaknesses. Therefore, choosing the most suitable technique for practical applications usually depends on specific requirements and contextual conditions. X

Enhancing Biometric Security with Bimodal Deep Learning and Feature-level Fusion of Facial and Voice Data

Recent research in biometric technologies underscores the benefits of multimodal systems that use multiple traits to enhance security by complicating the replication of samples from genuine users. To address this, we present a bimodal deep learning network (BDLN or BNet) that integrates facial and voice modalities. Voice features are extracted using the SincNet architecture, and facial image features are obtained from convolutional layers. Proposed network fuses these feature vectors using either averaging or concatenation methods. A dense connected layer then processes the combined vector to produce a dual-modal vector that encapsulates distinctive user features. This dual-modal vector, processed through a softmax activation function and another dense connected layer, is used for identification. The presented system achieved an identification accuracy of 99% and a low equal error rate (EER) of 0.13% for verification. These results, derived from the VidTimit and BIOMEX-DB datasets, highlight the effectiveness of the proposed bimodal approach in improving biometric security.

Live feature transformation scale extraction of face images based on deep learning algorithm

In view of the low extraction accuracy and efficiency of the current feature extraction methods of face images, a method for extracting the scale of live feature transformation of face images based on deep learning algorithm is proposed. First, the deep learning method is used to complete the denoising of face images; then the Gabor wave filter is used to decompose the face signal, and it is input into the deep subspace model to complete the extraction of feature transformation scale; finally, the live feature transformation scale extraction of face images is completed based on (particle PSO swarm optimization algorithm). Experimental results show that the proposed method for extracting face image features has higher accuracy, faster recognition speed and better overall application effect.

Study of Multimodal Identification Algorithms using Modern Methods and Tools of Multivariate Analysis

This article aims to comprehensively investigate the theoretical and practical foundations, as well as the distinctive characteristics, underpinning the study of multimodal identification algorithms. This investigation will be conducted using state-of-the-art methods and tools of multidimensional analysis. The development of a multimodal algorithm using the method of modality fusion at the feature level encompasses the integration of various algorithms rooted in multivariate analysis. These include a combined voice activity detector, a face detector utilizing the MTCNN (multi-task cascade convolutional networks) architecture, fine-frequency cepstral coefficients, facial image features, and a decision-making module. To construct a multimodal identification algorithm, a framework for combining these algorithms based on multivariate analysis is proposed. Analysis of the acquired data indicates that “Test 1”, utilizing facial image data, exhibits the highest performance indicators, approaching nearly 100%. Tests 2 and 3 involving voice signals exhibit a minor error in the pre-processing stage, attributed to the inherent delay experienced by participants during the video conference. The proposed multimodal algorithm, integrated within a biometric identification system, enables successful user verification research through the utilization of a combined multidimensional analysis algorithm. Furthermore, the algorithm showcases superior research outcomes in comparison to other analogous multimodal identification algorithms, as it yields precise results.

Personalized hairstyle and hair color editing based on multi-feature fusion

In the metaverse era, virtual design of hairstyle becomes very popular for personalized aesthetics. As hair design tasks can be decomposed into hair attribute editing and generation, the development of generative adversarial networks (GANs) has significantly prompted its development. The majority of the existing algorithms focus on transferring the overall hair region from one face to another, which ignore fine control over the color and geometric features. Furthermore, these algorithms may result in unnatural generation results. In this paper, we propose a hair modification framework that learns hairstyle information from a reference face mask and color information from a guidance face image. Firstly, the features of the input face image and reference images are extracted through a group of encoders, and then divided into feature vectors of coarse, medium, and fine levels. Secondly, multi-level feature vectors are fused in the latent space using attention-based modulation modules. Finally, the fused feature vector is passed through a StyleGAN generator to generate face images with specified hairstyle and hair color. Experimental results show that the proposed method can finely simulate the hairstyle transition between long and short hair under the constraint of the reference mask, and can produce realistic fusion effects in the hair-covered regions, such as ears, neck, and forehead. Various hair dyeing effects that adapt to personalized characteristics are demonstrated, as facial features including skin color and hair texture are preserved when transferring the hair color.

The Effects of AI-Driven Face Restoration on Forensic Face Recognition

In biometric recognition, face recognition is a mature and widely used technique that provides a fast, accurate, and reliable method for human identification. This paper aims to study the effects of face image restoration for forensic face recognition and then further analyzes the advantages and limitations of the four state-of-the-art face image restoration methods in the field of face recognition for forensic human image identification. In total, 100 face image materials from an open-source face image dataset are used for experiments. The Gaussian blur processing is applied to simulate the effect of blurred face images in actual cases of forensic human image identification. Four state-of-the-art AI-driven face restoration methods are used to restore the blurred face images. We use three mainstream face recognition systems to evaluate the recognition performance changes of the blurred face images and the restored face images. We find that although face image restoration can effectively remove facial noise and blurring effects, the restored images do not significantly improve the recognition performance of the face recognition systems. Face image restoration may change the original features in face images and introduce new made-up image features, thereby affecting the accuracy of face recognition. In current conditions, the improvement in face image restoration on the recognition performance of face recognition systems is limited, but it still has a positive role in the application of forensic human image identification.

A software package for the investigation of spatio-temporal feature descriptors

At present time researchers investigate approaches to the automatic facial micro-expression analysis to implement the possibility of the recognition of true human emotions from the image sequence of his face. The modern technologies are based on the use of complex mathematical algorithms with many adjusted parameters; in particular, such algorithms are the algorithms of spatio-temporal feature descriptors. The scientists who are not technical specialists, e.g., psychologists or psychotherapists, when investigating these algorithms may encounter difficulties in writing the program code. Therefore, it became necessary to develop a special software package for the investigation of spatio-temporal feature descriptors. Aim of the work is development of a special software package for studying various spatial and temporal descriptors of facial micromovement features in video images A software package for the investigation of spatio-temporal feature descriptors with a graphical interface is developed. The software package makes it possible to investigate the influence of the selection of the descriptor (LBP-TOP, FHOOF or FHOFO) and its parameters on the micro-facial movement detection accuracy for different input datasets of human face image sequences. The developed software package for the investigation of spatio-temporal feature descriptors can be applied as a software tool for carrying out investigations in the facial micro-expression analysis field.

Gender Classification through Fusion of Holistic and Region-based Facial Patterns

In this study, a robust gender prediction system is proposed to fuse global and regional facial representations through score and feature level fusion. In order to extract facial features for gender classification, Binarized Statistical Image Features (BSIF) approach is applied on holistic and regional features of face images. The extracted features are then concatenated to combine the region-based information at feature level fusion. Then the optimized sub-set of features is selected using Particle Swarm Optimization (PSO) method. Finally, the holistic and regional features are combined at score level fusion to produce the final set of scores for gender classification. This study applies Weighted Sum (WS) rule strategy for score level fusion. The experimental results are performed on Multiple Biometric Grand Challenge (MBGC) and CASIA-Iris-Distance databases with consideration of subject-disjoint training and testing evaluation to testify the validity of the proposed gender classification system. The experimental results of the study demonstrate the success of the proposed scheme for gender prediction.

Air Traffic Controller Fatigue Detection by Applying a Dual-Stream Convolutional Neural Network to the Fusion of Radiotelephony and Facial Data

The role of air traffic controllers is to direct and manage highly dynamic flights. Their work requires both efficiency and accuracy. Previous studies have shown that fatigue in air traffic controllers can impair their work ability and even threaten flight safety, which makes it necessary to carry out research into how to optimally detect fatigue in controllers. Compared with single-modality fatigue detection methods, multi-modal detection methods can fully utilize the complementarity between diverse types of information. Considering the negative impacts of contact-based fatigue detection methods on the work performed by air traffic controllers, this paper proposes a novel AF dual-stream convolutional neural network (CNN) architecture that simultaneously extracts controller radio telephony fatigue features and facial fatigue features and performs two-class feature-fusion discrimination. This study designed two independent convolutional processes for facial images and radio telephony data and performed feature-level fusion of the extracted radio telephony and facial image features in the fully connected layer, with the fused features transmitted to the classifier for fatigue state discrimination. The experimental results show that the detection accuracy of radio telephony features under a single modality was 62.88%, the detection accuracy of facial images was 96.0%, and the detection accuracy of the proposed AF dual-stream CNN network architecture reached 98.03% and also converged faster. In summary, a dual-stream network architecture based on facial data and radio telephony data is proposed for fatigue detection that is faster and more accurate than the other methods assessed in this study.

Generative adversarial networks with attentional multimodal for human face synthesis

<p><span>Face synthesis and editing has increased cumulative consideration by the improvement of generative adversarial networks (GANs). The proposed attentional GAN-deep attentional multimodal similarity modal (AttnGAN-DAMSM) model focus on generating high-resolution images by removing discriminator components and generating realistic images from textual description. The attention model creates the attention map on the image and automatically retrieves the features to produce various sub-areas of the image. The DAMSM delivers fine-grained image-text identical loss to generative networks. This study, first describe text phrases and the model will generate a photorealistic high-resolution image composed of features with high accuracy. Next, model will fine-tune the selected features of face images and it will be left to the control of the user. The result shows that the proposed AttnGAN-DAMSM model delivers the performance metrics like structural similarity index measure (SSIM), feature similarity index measure (FSIM) and frechet inception distance (FID) using CelebA and CUHK face sketch (CUFS) dataset. For CelebFaces attribute (CelebA) dataset, the SSIM achieves 78.82% and for CUFS dataset, the SSIM achieves 81.45% which ensures accurate face synthesis and editing compared with existing methods such as GAN, SuperstarGAN and identity-sensitive GAN (IsGAN) models.</span></p>

Integration of Colbp and Viola Jones Feature Extraction Methods in Gender Classification Based on Facial Image

Nowadays face recognition still being a hot topics to be discussed especially it’s utility for gender classification. Gender classification is an easy task for human but it’s a challenging task for computers because it doesn’t have capability for recognizing human gender without feature extraction. There are still many researches about facial image feature extraction for gender classification. Geometry features and Texture Features are well perform features for gender classification. This paper will presents fusion of those feature in order to find an improvement for gender classifications task. Each features will be extracted using Viola Jones Algorithm and Compass Local Binary Pattern method. Both features will be combined using concatenated method in dataframe format. Viola Jones algorithm has an issues when detecting each facial regions so it causes outliers in each geometry features. The proposed method will be evaluated on color FERET dataset that contains facial images. Classification task will be done using Random Forest and Backpropagation. The result is fusion features present an improvement in gender classification using Backpropagation with 87% accuracy. It prove that proposed method perform better than single feature extraction method.

Applicability of deep learning for blood pressure estimation during hemodialysis based on facial images

Back groundIn hemodialysis, hypotension occurs due to dehydration and solute removal. Conventional blood pressure monitoring during dialysis is intermittent and relies on staff experience and intuition to predict patient blood pressure trends based on the amount of water removed on the day and previous trends, which requires hemodialysis operations that do not lead to hypotension. Our research group has attempted to estimate blood pressure based on the spatial features of facial visible images, including information on facial color, and facial infrared images, including information on skin temperature. It is expected to realize early detection of blood pressure decrease during treatment if the blood pressure of dialysis patients can be estimated from their facial visible and infrared images measured continuously and remotely. In this study, we verified the applicability of deep learning algorithms in blood pressure estimation based on facial visible and infrared images of hemodialysis patients.MethodsMeasured facial visible and infrared images and mean blood pressure (MBP) of hemodialysis patients were applied to a convolutional neural network to construct an MBP estimation model based on the spatial features of the facial images.ResultsAverage blood pressure could be estimated with an error of less than 20 mmHg based on the spatial features of the facial images, and the blood pressure estimation accuracy based on the spatial features of the facial infrared images was higher than that of the facial visible images.ConclusionWe found the possibility of applying the deep learning algorithm to blood pressure estimation based on the spatial features of facial images.Trial registration This study is not subject to enrollment in a clinical trial due to the absence of both intervention and invasion. The Ethics Review Committee of Jichi Medical University has approved the same interpretation.

Performance Analysis of Facial Image Feature Extraction Algorithm for Smart Home Security System Detection

Alongside the development of technology to facilitate multi-family security, security tools are also being developed. Smart home security is one of the very popular security tools in Indonesian home construction. The tool works automatically in real time and has no restrictions on environmental conditions. However, currently available tools still lack consistent accuracy and consistent performance. To solve this problem, the author proposes a smart home security system with an Arduino UNO-connected camera, two relay modules, a magnetic lock, and connecting to a home Internet of Things system. The methods used in the research for this thesis project were: 1. Literature review of ongoing Smart Home Security using facial image feature extraction algorithm research; 2. Deployment of Arduino UNO, 2 Relay Module, and Solenoid Lock; 3. The feature extraction algorithm used is Wavelet. The proposed method is expected to achieve an accuracy of 80% or more. The experimental results showed that the proposed prototype of this experiment achieved the accuracy of 85.7%. In addition to accuracy, there is also precision rate at 87.94%, recall rate at 87.56%, and f1-score rate at 87.28%

Enhancing facial feature de-identification in multiframe brain images: A generative adversarial network approach.

The collection of head images for public datasets in the field of brain science has grown remarkably in recent years, underscoring the need for robust de-identification methods to adhere with privacy regulations. This paper elucidates a novel deep learning-based approach to deidentifying facial features in brain images using a generative adversarial network to synthesize new facial features and contours. We employed the precision of the three-dimensional U-Net model to detect specific features such as the ears, nose, mouth, and eyes. Results: Our method diverges from prior studies by highlighting partial regions of the head image rather than comprehensive full-head images. We trained and tested our model on a dataset comprising 490 cases from a publicly available head computed tomography image dataset and an additional 70 cases with head MR images. Integrated data proved advantageous, with promising results. The nose, mouth, and eye detection achieved 100% accuracy, while ear detection reached 85.03% in the training dataset. In the testing dataset, ear detection accuracy was 65.98%, and the validation dataset ear detection attained 100%. Analysis of pixel value histograms demonstrated varying degrees of similarity, as measured by the Structural Similarity Index (SSIM), between raw and generated features across different facial features. The proposed methodology, tailored for partial head image processing, is well suited for real-world imaging examination scenarios and holds potential for future clinical applications contributing to the advancement of research in de-identification technologies, thus fortifying privacy safeguards.

Face spoofing detection using Heterogeneous Auto-Similarities of Characteristics

Facial recognition systems are widely used in various daily applications due to their high accuracy rate and ease of use. Unfortunately, their performance drops drastically when encountering spoofing attacks. In this paper, a novel pipeline for face spoofing detection based on the extraction of Heterogeneous Auto-Similarities of Characteristics (HASC) descriptor from the HSV and YCbCr color spaces is proposed. To detect print and replay spoofing attacks, the HASC descriptor encodes linear and non-linear dependencies between low-level dense features of the color face image using covariance and information-theoretic measures. The combination of covariance and information-theoretic measures makes HASC an efficient descriptor to explore how the features are interrelated in real and fake faces. The HASC texture descriptions are extracted separately from each color channel of the HSV and YCbCr color spaces, and the resulting feature vectors are concatenated into an extended feature vector to better discriminate between real and fake faces. Extensive experiments are conducted on publicly available databases namely, Replay-Attack, CASIA FASD, and MSU MFSD. The obtained results demonstrate that while being of low dimension, the HASC descriptor when fed into a binary Support Vector Machine (SVM) classifier, achieves competitive results compared to the state-of-the-art.

Skinophile: The Art Of Skincare Selection With Haar Wavelet

In this study, a mobile application has been developed with the primary objective of classifying facial skin types to facilitate the selection of appropriate skincare products tailored to individual skin needs. The motivation behind this research stems from the widespread issue of individuals being unaware of their specific skin types, which often leads to suboptimal skincare routines and, in some cases, adverse effects such as skin dullness and damage. To address this challenge, a comprehensive problem-solving approach has been adopted. The methodology encompasses the utilization of the Haar Wavelets method for facial image feature extraction, complemented by the application of the Support Vector Machine (SVM) algorithm for precise skin type classification. Face Recognition technology has also been integrated to enhance the accuracy and reliability of the classification process.In terms of system development, an Agile methodology has been employed, facilitating swift and cost-effective project completion. This agile approach ensures the development process is efficient, reducing both time and financial resources.The mobile application developed for this purpose utilizes the Python programming language, specifically incorporating the PyWavelets library. The culmination of this research effort is a user-friendly mobile application that enables users to capture their facial images. Subsequently, the application employs advanced algorithms to identify their specific skin type, providing personalized recommendations for suitable skincare products and routines. This innovative solution aims to empower individuals with the knowledge and tools necessary to enhance the effectiveness of their skincare regimen, ultimately promoting healthier and more radiant skin.

Cephalometric landmark detection without X-rays combining coordinate regression and heatmap regression

Fully automated techniques using convolutional neural networks for cephalometric landmark detection have recently advanced. However, all existing studies have adopted X-rays. The problem of direct exposure of patients to X-ray radiation remains unsolved. We propose a model for detecting cephalometric landmarks using only facial profile images without X-rays. First, the model estimates the landmark coordinates using the features of facial profile images through high-resolution representation learning. Second, considering the spatial relationship of the landmarks, the model refines the estimated coordinates. The estimated coordinates are input into fully connected networks to improve the accuracy. During the experiment, a total of 2000 facial profile images collected from 2000 female patients were used. Experiments results suggested that the proposed method may perform at a level equal to or potentially better than existing methods using cephalograms. We obtained an MRE of 0.61 mm for the test data and a mean detection rate of 98.20% within 2 mm. Our proposed two-stage learning method enables a highly accurate estimation of the landmark positions using only facial profile images. The results indicate that X-rays may not be required when detecting cephalometric landmarks.

Face image generation and feature visualization using deep convolutional generative adversarial networks

Generative Neural Networks (GAN) aims to generate realistic and recognizable images, including portraits, cartoons and other modalities. Image generation has broad application prospects and important research value in the fields of public security and digital entertainment, and has become one of the current research hotspots. This article will introduce and apply an important image generation model called GAN, which stands for Generative Adversarial Network. Unlike recent image processing models such as Variational Autoencoders (VAE), The discriminative network evaluates potential candidates while the GAN generates candidates. As a result, the discriminative network distinguishes created and real candidates, while the generative network learns to map from a latent space to an interest data distribution. In this article, the GAN model and some of its extensions will be thoroughly applied and implemented based on the dataset of CelebA, and details will be discussed through the images and graphs generated by the model. Specific training methods for various models and optimization algorithms can be produced by the GAN framework. The experiment’s findings in this article will show how the framework’s potential may be quantified and qualitatively assessed using the samples that were produced.

DualGGAN: A New Facial Style Transfer Network

In response to the background penetration problem of unsupervised style transfer algorithms in most cases, a Transformer style transfer network DualGGAN based on dual generators and fusion of relative position encoding is proposed. The network is trained using the least squares generative adversarial network, and the neural network is used as the image feature extractor to generate feature maps to obtain facial image features with attention weights from the feature maps, utilizing relative position encoding and mask loss to jointly constrain feature region style transfer. The experimental results show that the DualGGAN network effectively reduces artifact generation when implementing facial style transfer, maintains good background consistency, and has good generalization ability. Experiments have shown that the FID and KID indicators on the cat2dog and older2adult datasets are significantly improved compared to other algorithms.

Fuzzy Preprocessing of Viola-Jones Algorithm for Face Recognition in Thermal Images

This paper proposes to improve the Viola-Jones algorithm to identify facial features in thermal images using a fuzzy linguistic modifier. The purpose is to improve the image quality in the infrared plane and to facilitate the recognition stage, feature selection and emotion classification. The results obtained show that adding a preprocessing stage improves the capability of the Viola-Jones algorithm in identifying facial features in thermal images.