Facial Feature Extraction Research Articles

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.

Face Authentication using MTCNN and FaceNet

Abstract: Advancements in facial recognition are on the rise after the recent breakthroughs in deep learning technologies and the extensive training datasets. But still, the practical application of facial recognition for authentication purposes faces some difficulties and problems when dealing with variations that are observed in real-world scenarios, including variations in facial poses, varying angles, lightning conditions and potential obstructions. In order to resolve these challenges, this paper presents an innovative deep learning API, which combines the capabilities of MTCNN and FaceNet, to overcome these limitations. Building upon the foundations of MTCNN and FaceNet, we have developed an API capable of verifying the identity of users through a two-step verification process. The system utilizes MTCNN for precise face detection and FaceNet for facial feature extraction that has the ability to directly map facial images into a compact Euclidean space in which spatial distances directly reflect facial similarity, and then storing this data securely within a MongoDB database. We have used the cutting-edge Deep Face model, developed by Facebook’s AI research team. Its reliable deep learning capabilities have helped us in achieving exceptional accuracy in the user’s verification process. This system serves to provides a superior level of user authentication, effectively reducing the risk of an unauthorized access while optimizing the user experience. Even though there is still some room for improvement within our experiment, we are committed to providing a comprehensive summary of our findings and an exploration of the challenges on the horizon.

Expression Recognition of Multiple Faces Using a Convolution Neural Network Combining the Haar Cascade Classifier

Facial expression serves as the primary means for humans to convey emotions and communicate social signals. In recent years, facial expression recognition has become a viable application within medical systems because of the rapid development of artificial intelligence and computer vision. However, traditional facial expression recognition faces several challenges. The approach is designed to investigate the processing of facial expressions in real-time systems involving multiple individuals. These factors impact the accuracy and robustness of the model. In this paper, we adopted the Haar cascade classifier to extract facial features and utilized convolutional neural networks (CNNs) as the backbone model to achieve an efficient system. The proposed approach achieved an accuracy of approximately 70% on the FER-2013 dataset in the experiment. This result represents an improvement of 7.83% compared to that of the baseline system. This significant enhancement improves the accuracy of facial expression recognition. Herein, the proposed approach also extended to multiple face expression recognition; the module was further experimented with and obtained promising results. The outcomes of this research will establish a solid foundation for real-time monitoring and prevention of conditions such as depression through an emotion alert system.

Human vs machine towards neonatal pain assessment: A comprehensive analysis of the facial features extracted by health professionals, parents, and convolutional neural networks

Neonates are not able to verbally communicate pain, hindering the correct identification of this phenomenon. Several clinical scales have been proposed to assess pain, mainly using the facial features of the neonate, but a better comprehension of these features is yet required, since several related works have shown the subjectivity of these scales. Meanwhile, computational methods have been implemented to automate neonatal pain assessment and, although performing accurately, these methods still lack the interpretability of the corresponding decision-making processes. To address this issue, we propose in this work a facial feature extraction framework to gather information and investigate the human and machine neonatal pain assessments, comparing the visual attention of the facial features perceived by health-professionals and parents of neonates with the most relevant ones extracted by eXplainable Artificial Intelligence (XAI) methods, considering the VGG-Face and N-CNN deep learning architectures. Our experimental results show that the information extracted by the computational methods are clinically relevant to neonatal pain assessment, but yet do not agree with the facial visual attention of health-professionals and parents, suggesting that humans and machines can learn from each other to improve their decision-making processes. We believe that these findings might advance our understanding of how humans and machines code and decode neonatal facial responses to pain, enabling further improvements in clinical scales widely used in practical situations and in face-based automatic pain assessment tools as well.

A Novel Approach for Human Face Extraction and Detection using SAE-AFB-RFCN Framework

Research into Facial Recognition Technology (FRT), which uses a person's face to identify them, has become a hot topic among scientists. Face recognition relies heavily on feature extraction and classifiers. Occlusion, illuminations, and a complicated background provide the most difficult problems for face recognition systems to overcome. With the advent of Artificial Intelligence and Deep Learning techniques now it became easy to identify different features of an Image and to detect a face. In this paper, Stacked Auto Encoder (SAE), Artificial Feeding Bird (AFB), Region Based Fully Convolutional Network (RFCN), novel approach is developed for human face feature extraction and detection. Initially, the dataset is normalized using rescaling method. Then the Stacked Auto Encoder with Artificial Feeding Birds (SAE-AFB) optimization algorithm is used for facial feature extraction and Region based Fully Convolutional Networks (R-FCN) algorithm is used for detection and classification. The WIDER Face dataset is used for training and testing. Experimental results demonstrate that the proposed SAE-AFB-RFCN framework outperforms the existing algorithms in terms of accuracy, precision, recall and F1-score.

A Real-Time Emotion Detection System from Facial Expression Using Convolutional Neural Network

Abstract: This paper presents an abstract-real-time facial emotion detection system utilizing convolutional neural networks (CNNs) for robust face recognition, addressing the demand for rapid and accurate emotion classification in dynamic contexts. The proposed CNN-based approach ensures real-time processing while maintaining high accuracy, detailing the architecture of a specialized CNN model tailored for real-time emotion detection. The study optimizes network layers and parameters for efficient facial feature extraction and analysis, substantiated through extensive experiments on diverse datasets, showcasing the system's instant emotion detection proficiency. Rigorous quantitative analysis demonstrates its superior performance compared to existing methods, underscoring its effectiveness. The system's potential to precisely detect real-time emotions holds applications in interactive interfaces, spanning from immersive virtual environments to highly responsive human-computer interactions.

Enhancing Attendance Management through Real-Time Face Recognition using combination of LBP Histogram and Linear Discriminant Analysis

Face recognition technology's excellent accuracy and non-intrusiveness have made it a major player in the attendance management space. This research study presents an advanced method of tracking attendance through the integration of a Face Recognition System (FRS) with Linear Discriminant Analysis (LDA) and Local Binary Pattern (LBP) histogram analysis. The architecture of the suggested attendance system is described in the study, with an emphasis on how LDA and LBP techniques are used to increase the accuracy of face feature extraction and recognition. Through the use of LDA, the system improves face data's discriminative strength, producing attendance records that are more trustworthy. Robust facial texture and pattern identification are simultaneously made possible by LBP histogram analysis, particularly in a variety of environmental settings. To sum up, this study advances biometric attendance systems by demonstrating how LDA and LBP histogram analysis may improve the precision and dependability of face recognition systems used for attendance tracking

Gravitational Deep Convoluted Stacked Kernel Extreme Learning Based Classification for Face Recognition

In recent times, researchers have designed several deep learning (DL) algorithms and specifically face recognition (FR) made an extensive crossover. Deep Face Recognition systems took advantage of the hierarchical framework of the DL algorithms to learn discriminative face characterization. However, when handling severe occlusions in a face, the execution of present-day methods reduces appreciably. Several prevailing works regard that, when face recognition is taken into consideration, affinity materializes to be a pivotal recognition feature. However, the rate of affinity changes when the face image for recognition is found to be illuminated, and occluded, with changes in the age of the subject. Motivated by these issues, in this work a novel method called Gravitational Deep Convoluted Stacked Kernel Extreme Learning-based (GDC-SKEL) classification for face recognition is proposed for human face recognition problems in frontal views with varying age, illumination, and occlusion. First, with the face images provided as input, Gravitational Center Loss-based Face Alignment model is proposed to minimize the intra-class difference, which can overcome the influence of occlusion in face images. Second, Deep Convoluted Tikhonov Regularization-based Facial Region Feature extraction is applied to the occlusion-removed face images. Here, by employing the Convoluted Tikhonov Regularization function, salient features are said to be extracted with an age-invariant representation. Finally, Stacked Kernel Extreme Learning-based Classification is designed. The extracted features are given to the Stacked Kernel Extreme Learning-based Classification and to identify testing samples Stacked Kernel is utilized. The performance of GDC-SKEL is evaluated on Cross-Age Celebrity Dataset. Experimental results are compared with other state-of-the-art classifiers in terms of face recognition accuracy, face recognition time, PSNR, and False Positive Rate which shows the effectiveness of the proposed GDC-SKEL classifier.

Extraction and analysis of factors influencing college students’ mental health based on deep learning model

Abstract This paper analyzes the factors that affect the mental health of college students, and the focus of this analysis is on emotional-emotional factors. The extroverted presentation of emotional affects is used as visual information to study the mental health status of college students. Based on the advantages of long and short memory neural networks based on deep learning models in processing two-dimensional images, a computer vision task is used to perform visual recognition, target detection, and expression image classification of college students’ facial expressions. The use of video facial expression recognition with multi-feature fusion is utilized to effectively identify the facial expression machine of college students in both laboratory-controlled and outdoor environments. The mental health status of college students was analyzed in terms of facial expression recognition and feature extraction. The recognition rate for general features was 80.3%, 89.3% for six specific facial emotions, and 84.4% for LBP features.

Research on face emotion recognition algorithm based on deep learning neural network

Abstract This paper explores face emotion recognition based on deep learning neural networks in order to make the computer recognize facial emotion more accurately. Firstly, we introduce the convolutional neural network, which consists of a convolutional layer, pooling layer and fully connected layer in the deep learning neural network, and propose the variational self-encoder face emotion recognition algorithm based on the convolutional neural network in combination with the facial emotion recognition process. The correct recognition rates achieved by EVA using 32×32 pixels, LBP features and 50-dimensional Gabor wavelet features are 95.13%, 96.74% and 98.84%, respectively. In terms of emotion recognition ability, the recognition accuracy percentages were around 70% for large emotion fluctuations and around 30% for small emotion fluctuations. Since the neutral facial emotion is not well distinguished, the recognition degree of neutral facial emotion is only 80%, and the recognition degree using the self-encoder-based face emotion recognition algorithm in several facial features extraction is above 80%, and the recognition degree with neutral emotion removed is up to about 90%. Therefore, the algorithm in this paper has good classification performance and can recognize and analyze facial emotion features accurately.

Research on Fatigued-Driving Detection Method by Integrating Lightweight YOLOv5s and Facial 3D Keypoints.

In response to the problem of high computational and parameter requirements of fatigued-driving detection models, as well as weak facial-feature keypoint extraction capability, this paper proposes a lightweight and real-time fatigued-driving detection model based on an improved YOLOv5s and Attention Mesh 3D keypoint extraction method. The main strategies are as follows: (1) Using Shufflenetv2_BD to reconstruct the Backbone network to reduce parameter complexity and computational load. (2) Introducing and improving the fusion method of the Cross-scale Aggregation Module (CAM) between the Backbone and Neck networks to reduce information loss in shallow features of closed-eyes and closed-mouth categories. (3) Building a lightweight Context Information Fusion Module by combining the Efficient Multi-Scale Module (EAM) and Depthwise Over-Parameterized Convolution (DoConv) to enhance the Neck network's ability to extract facial features. (4) Redefining the loss function using Wise-IoU (WIoU) to accelerate model convergence. Finally, the fatigued-driving detection model is constructed by combining the classification detection results with the thresholds of continuous closed-eye frames, continuous yawning frames, and PERCLOS (Percentage of Eyelid Closure over the Pupil over Time) of eyes and mouth. Under the premise that the number of parameters and the size of the baseline model are reduced by 58% and 56.3%, respectively, and the floating point computation is only 5.9 GFLOPs, the average accuracy of the baseline model is increased by 1%, and the Fatigued-recognition rate is 96.3%, which proves that the proposed algorithm can achieve accurate and stable real-time detection while lightweight. It provides strong support for the lightweight deployment of vehicle terminals.

A Review of Latest Trends and Technologies in the Field of Facial Re-recognition in Surveillance Cameras

Deep Learning-driven advances in facial re-identification are transforming computer vision. The increasing need for precise identity identification in surveillance, law enforcement, and public safety is being met by this advancement. Complex face feature extraction is automated by Deep Learning, particularly with Convolutional Neural Networks (CNNs), improving recognition under difficult circumstances. This paper focuses on the most recent developments and the influence of Deep Learning on facial re-identification. For scholars, practitioners, politicians, and industry actors, it's a vital resource that emphasizes the need of staying current for the best possible system use. To sum up, the combination of Deep Learning with facial re-identification offers accurate, dependable, and effective identity recognition. It is essential for security, law enforcement, and public safety that technology advances.

Novel Multi-Feature Fusion Facial Aesthetic Analysis Framework

Machine learning has been used in facial beauty prediction studies. However, the integrity of facial geometric information is not considered in facial aesthetic feature extraction, and the impact of other facial attributes (expression) on aesthetics. We propose a novel multi-feature fusion facial aesthetic analysis framework (NMFA) to overcome this problem. First, we designed a facial shape feature, which is an intuitive, visual quantitative description, based on B-spline. Second, we designed a representative low-dimensional facial structural feature to establish the theoretical basis of the facial structure, based on facial aesthetic structure and expression recognition theory. Next, we designed texture and holistic features based on Gabor and VGG-face network. Finally, we used a multi-feature fusion strategy to fuse them for aesthetic evaluation. Experiments were conducted on four databases. The results revealed that the proposed method realizes the visualization of facial shape features, enriches geometric information, solves the problem of lack of facial geometric information and difficulty to understand, and achieves excellent performance with fewer parameters.

Development of Real-Time Emotion Recognition System Using Facial Expressions

This research presents a real-time emotion recognition system that combines human-friendly machine interaction with picture processing. For many years, facial detection has been available. Moving further, it is possible to simulate the emotions that people express on their faces and experience in their brains through the use of video, electric signals, or image forms. Since it is hard for computers to detect emotions from images or videos and a difficult task for the human eye, machine emotion detection requires a variety of image processing approaches for feature extraction. The approach proposed in this paper consists of two primary processes: facial expression recognition (FER) and face detection. The experimental investigation of facial emotion recognition is the main topic of this study. An emotion detection system's workflow consists of face detection, feature extraction, pre-processing, classification, and image acquisition. The emotion identification system uses the Haar cascade algorithm, an object detection algorithm, to recognize faces in an image or a real-time video, and the KNN Classifier for image classification in order to identify such emotions. Using the webcam to capture real-time photos, this system operates. The goal of this research is to develop an automatic facial expression recognition system that can recognize various emotions. Based on these studies, the system may be able to distinguish between a number of people who are fearful, furious, shocked, sad, or pleased, among other emotions.

Face Recognition approach via Deep and Machine Learning

Face recognition is a biometric technology that involves identifying and verifying individuals based on their facial features. It finds applications in security, surveillance, and user authentication systems. The extraction of facial image features and classifier selection are more challenging to identify with conventional facial recognition technologies, and the recognition rate is lower. The paper present proposed model combined between deep wavelet scattering transform network regarding the extraction of features and machine learning for classification purposes. The proposed model consists four stage: obtaining images, performing pre-processing, extracting features, and then applying classification techniques. using both SoftMax classifier (part of deep learning model) and Support Vector Machine classifier (SVM). We used property collected dataset called MULB dataset. The experimental result shows that SVM classifier provide better results than SoftMax classifier. The results from the experiments conducted on the MULB face database showcased the efficacy of the suggested face recognition approach. The proposed method achieved an outstanding recognition accuracy of 98.29% with SVM classifier and 97.87% with SoftMax classifier.

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.

Face Detection Based on DF-Net

Face data have found increasingly widespread applications in daily life. To efficiently and accurately extract face information from input images, this paper presents a DF-Net-based face detection approach. A lightweight facial feature extraction neural network based on the MobileNet-v2 architecture is designed and implemented. By incorporating multi-scale feature fusion and spatial pyramid modules, the system achieves face localization and extraction across multiple scales. The proposed network is trained on the open-source face detection dataset WiderFace. The hyperparameters such as bottleneck coefficients and quality factors are discussed. Comparative experiments with other commonly used networks are carried out in terms of network model size, processing speed, and network extraction accuracy. Experimental results affirm the efficacy and robustness of this method, especially in challenging facial poses.

Hybrid Techniques of Facial Feature Image Analysis for Early Detection of Autism Spectrum Disorder Based on Combined CNN Features.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by difficulties in social communication and repetitive behaviors. The exact causes of ASD remain elusive and likely involve a combination of genetic, environmental, and neurobiological factors. Doctors often face challenges in accurately identifying ASD early due to its complex and diverse presentation. Early detection and intervention are crucial for improving outcomes for individuals with ASD. Early diagnosis allows for timely access to appropriate interventions, leading to better social and communication skills development. Artificial intelligence techniques, particularly facial feature extraction using machine learning algorithms, display promise in aiding the early detection of ASD. By analyzing facial expressions and subtle cues, AI models identify patterns associated with ASD features. This study developed various hybrid systems to diagnose facial feature images for an ASD dataset by combining convolutional neural network (CNN) features. The first approach utilized pre-trained VGG16, ResNet101, and MobileNet models. The second approach employed a hybrid technique that combined CNN models (VGG16, ResNet101, and MobileNet) with XGBoost and RF algorithms. The third strategy involved diagnosing ASD using XGBoost and an RF based on features of VGG-16-ResNet101, ResNet101-MobileNet, and VGG16-MobileNet models. Notably, the hybrid RF algorithm that utilized features from the VGG16-MobileNet models demonstrated superior performance, reached an AUC of 99.25%, an accuracy of 98.8%, a precision of 98.9%, a sensitivity of 99%, and a specificity of 99.1%.

Face Quality Detection in a Video Frame

Abstract: Face detection technology is often used for surveillance of detecting and tracking of people in real time. The applications using these algorithms deal with low quality video feeds having less Pixels Per Inch (ppi) and/or low frame rate. The algorithms perform well with such video feeds, but their performance deteriorates towards high quality, high data-per-frame videos. This project focuses on developing such an algorithm that gives faster results on high quality videos, at par with the algorithms working on low quality videos. The proposed algorithm uses MTCNN as base algorithm, and speeds it up for highdefinition videos. This project also presents a novel solution to the problem of occlusion and detecting faces in videos. This survey provides an overview of the face detection from video literature, which predominantly focuses on visible wavelength face video as input. For the high-quality videos, we will Face-MTCNN and KLT, for low quality videos we will use MTCNN and KLT. Open issues and challenges are pointed out, i.e., highlighting the importance of comparability for algorithm evaluations and the challenge for future work to create Deep Learning (DL) approaches that are interpretable in addition to Track the faces. The suggested methodology is contrasted with conventional facial feature extraction for every frame and with well-known clustering techniques for a collection of videos

Multimodal Attention Dynamic Fusion Network for Facial Micro-Expression Recognition.

The emotional changes in facial micro-expressions are combinations of action units. The researchers have revealed that action units can be used as additional auxiliary data to improve facial micro-expression recognition. Most of the researchers attempt to fuse image features and action unit information. However, these works ignore the impact of action units on the facial image feature extraction process. Therefore, this paper proposes a local detail feature enhancement model based on a multimodal dynamic attention fusion network (MADFN) method for micro-expression recognition. This method uses a masked autoencoder based on learnable class tokens to remove local areas with low emotional expression ability in micro-expression images. Then, we utilize the action unit dynamic fusion module to fuse action unit representation to improve the potential representation ability of image features. The state-of-the-art performance of our proposed model is evaluated and verified on SMIC, CASME II, SAMM, and their combined 3DB-Combined datasets. The experimental results demonstrated that the proposed model achieved competitive performance with accuracy rates of 81.71%, 82.11%, and 77.21% on SMIC, CASME II, and SAMM datasets, respectively, that show the MADFN model can help to improve the discrimination of facial image emotional features.